Selecting annotations for training images using a neural network

ABSTRACT

Apparatuses, systems, and techniques to select labels of training images to train a network. In at least one embodiment, one or more labels of training images are selected to train a network.

TECHNICAL FIELD

At least one embodiment pertains to processing resources to selectlabels for training images to train a network. For example, at least oneembodiment pertains to processors or computing systems used to selectlabels for training images to train a network according to various noveltechniques described herein.

BACKGROUND

Selecting labels for training images to train a network uses significantmemory, time, or computing resources. Amounts of memory, time, orcomputing resources used to select labels for training images to train anetwork is improved.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a diagram of a model and training scheme, accordingto at least one embodiment;

FIG. 2 illustrates a diagram of a model and training scheme, accordingto at least one embodiment;

FIG. 3 shows an illustrative example of a process to select anannotation to train a neural network, according to at least oneembodiment;

FIG. 4 shows an illustrative example of a process to process trainingdata and update a model, according to at least one embodiment;

FIG. 5 illustrates a diagram of a comparison of learning paradigms,according to at least one embodiment;

FIG. 6A illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 6B illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 7 illustrates training and deployment of a neural network,according to at least one embodiment;

FIG. 8 illustrates an example data center system, according to at leastone embodiment;

FIG. 9A illustrates an example of an autonomous vehicle, according to atleast one embodiment;

FIG. 9B illustrates an example of camera locations and fields of viewfor the autonomous vehicle of FIG. 9A, according to at least oneembodiment;

FIG. 9C is a block diagram illustrating an example system architecturefor the autonomous vehicle of FIG. 9A, according to at least oneembodiment;

FIG. 9D is a diagram illustrating a system for communication betweencloud-based server(s) and the autonomous vehicle of FIG. 9A, accordingto at least one embodiment;

FIG. 10 is a block diagram illustrating a computer system, according toat least one embodiment;

FIG. 11 is a block diagram illustrating a computer system, according toat least one embodiment;

FIG. 12 illustrates a computer system, according to at least oneembodiment;

FIG. 13 illustrates a computer system, according to at least oneembodiment;

FIG. 14A illustrates a computer system, according to at least oneembodiment;

FIG. 14B illustrates a computer system, according to at least oneembodiment;

FIG. 14C illustrates a computer system, according to at least oneembodiment;

FIG. 14D illustrates a computer system, according to at least oneembodiment;

FIGS. 14E and 14F illustrate a shared programming model, according to atleast one embodiment;

FIG. 15 illustrates exemplary integrated circuits and associatedgraphics processors, according to at least one embodiment;

FIGS. 16A-16B illustrate exemplary integrated circuits and associatedgraphics processors, according to at least one embodiment;

FIGS. 17A-17B illustrate additional exemplary graphics processor logicaccording to at least one embodiment;

FIG. 18 illustrates a computer system, according to at least oneembodiment;

FIG. 19A illustrates a parallel processor, according to at least oneembodiment;

FIG. 19B illustrates a partition unit, according to at least oneembodiment;

FIG. 19C illustrates a processing cluster, according to at least oneembodiment;

FIG. 19D illustrates a graphics multiprocessor, according to at leastone embodiment;

FIG. 20 illustrates a multi-graphics processing unit (GPU) system,according to at least one embodiment;

FIG. 21 illustrates a graphics processor, according to at least oneembodiment;

FIG. 22 is a block diagram illustrating a processor micro-architecturefor a processor, according to at least one embodiment;

FIG. 23 illustrates a deep learning application processor, according toat least one embodiment;

FIG. 24 is a block diagram illustrating an example neuromorphicprocessor, according to at least one embodiment;

FIG. 25 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 26 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 27 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 28 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with at least one embodiment;

FIG. 29 is a block diagram of at least portions of a graphics processorcore, according to at least one embodiment;

FIGS. 30A-30B illustrate thread execution logic including an array ofprocessing elements of a graphics processor core according to at leastone embodiment;

FIG. 31 illustrates a parallel processing unit (“PPU”), according to atleast one embodiment;

FIG. 32 illustrates a general processing cluster (“GPC”), according toat least one embodiment;

FIG. 33 illustrates a memory partition unit of a parallel processingunit (“PPU”), according to at least one embodiment;

FIG. 34 illustrates a streaming multi-processor, according to at leastone embodiment.

FIG. 35 is an example data flow diagram for an advanced computingpipeline, in accordance with at least one embodiment;

FIG. 36 is a system diagram for an example system for training,adapting, instantiating and deploying machine learning models in anadvanced computing pipeline, in accordance with at least one embodiment;

FIG. 37 includes an example illustration of an advanced computingpipeline 3610A for processing imaging data, in accordance with at leastone embodiment;

FIG. 38A includes an example data flow diagram of a virtual instrumentsupporting an ultrasound device, in accordance with at least oneembodiment;

FIG. 38B includes an example data flow diagram of a virtual instrumentsupporting an CT scanner, in accordance with at least one embodiment;

FIG. 39A illustrates a data flow diagram for a process to train amachine learning model, in accordance with at least one embodiment; and

FIG. 39B is an example illustration of a client-server architecture toenhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment.

DETAILED DESCRIPTION

In at least one embodiment, deep learning for medical image analysisprocesses various amounts of annotated data. In at least one embodiment,various natural language processing (NLP) algorithms are utilized toannotate medical images with data. In at least one embodiment,annotations from algorithm-based labelers are utilized by neuralnetworks to train networks to learn various patterns corresponding tovarious medical phenomena (e.g., diseases, injuries, etc.). In at leastone embodiment, medical images and annotations associated with medicalimages are fed to a neural network for training. In at least oneembodiment, neural network learns various patterns and determines anappropriate annotation for an image from a plurality of annotations toutilize for training along with classification training. In at least oneembodiment, neural network is trained to select which annotations arebest suited to use in training a neural network.

Neural networks are often trained to infer information from medicalimages. Often, the medical images come with many different labels to aidthe training. The labels may come from, for example, differentradiologists who have annotated the images, output from different NLPalgorithms processing doctor notes, etc., which may cause inherentambiguity, such as unavoidable noise and fuzziness within the annotationthemselves, when performing medical image analysis tasks. In at leastone embodiment, to improve a neural network's ability to inferinformation about an image, techniques described herein cause aprocessor with one or more circuits to, while training a neural networkwith labelled data, simultaneously train a neural network to select abest label from multiple available labels to use in training.

In at least one embodiment, a multi-label classification model istrained in connection with training data and corresponding annotations(e.g., labels). In at least one embodiment, a processor comprising oneor more circuits performs an attention-on-label scheme that utilizesmultiple NLP algorithms as auto-labelers, and selects a most reliableannotation from among various annotation to benefit training of a modelfor multi-label classification tasks. In at least one embodiment, anattention-on-label scheme is a scheme that selects an annotation that isbest suited to train a model, which may also be referred to as anattention-on-label training scheme, a meta-training scheme, anattention-on-label training method, an attention-on-label trainingprocess, an attention-on-label learning method, and/or variationsthereof. In at least one embodiment, an attention-on-label trainingscheme, in connection with a multi-label classification model, selectsannotations that provide a largest amount of information with gradientupdates for training of multi-label classification model. In at leastone embodiment, an attention-on-label training scheme comprises variousprocesses in which annotations of training data that are most relevantand/or appropriate for training a model (e.g., a multi-labelclassification model) are determined.

In at least one embodiment, an attention-on-label training schemeachieves various technical advantages, including but not limited to: anability to process training data with multiple noisy label sets toselect a best label to train a model, an ability to utilize a widevariability of multiple label sets and training data to produce anaccurate and robust model, an ability to collect data from and learnfrom noisy training data to train a model, and others. In at least oneembodiment, an attention-on-label training scheme attends labels thatmost benefit a model through various back-propagation processes. In atleast one embodiment, during each round of training, labels for an imageare compared to an output of a neural network being trained. In at leastone embodiment, a portion of neural network (e.g., parameters of neuralnetwork or a component of neural network) that selects labels is updatedaccording to how well labels match outputs from neural network. In atleast one embodiment, after multiple rounds of training, neural networkis able to select labels better, which aids in training when performingimage processing tasks. In at least one embodiment, anattention-on-label scheme is applied to a multi-label classificationtask utilizing various medical imagery.

FIG. 1 illustrates a diagram 100 of a model and training scheme, inaccordance with at least one embodiment. In at least one embodiment,diagram 100 includes a GPU 114 that is configured to train a model 108through training data 102 and annotations 104 obtained through a network106. In at least one embodiment, GPU 114 generates an updated model 110based on a determined best match annotation 112.

In at least one embodiment, graphics processing unit (GPU) 114 comprisesone or more graphics processing systems. In at least one embodiment, GPU114 is a Parallel Processing Unit (PPU). In at least one embodiment, GPU114 is one or more processors comprising one or more circuits thatimplements and trains various models and/or neural networks, such asmodel 108. In at least one embodiment, model 108 is a classificationmodel that identifies one or more categories that one or more featuresof input data belong to. In at least one embodiment, model 108 is amulti-label classification model. In at least one embodiment, model 108comprises one or more neural networks. In at least one embodiment, model108 comprises one or more convolutional neural network (CNN)architectures. In at least one embodiment, model 108 comprises one ormore neural networks that, based on input data, classify one or moreaspects of input data. In at least one embodiment, model 108 comprisesone or more neural networks that classify one or more features ofmedical imaging data. In at least one embodiment, model 108 comprisesvarious neural network, image feature detection, and embeddinggenerating components (further description with respect to model 108 isfound in description of FIG. 2).

In at least one embodiment, training data 102 and annotations 104 areobtained by GPU 114 through network 106. In at least one embodiment,network 106 represents any suitable path of communication between GPU114 and one or more other systems. In at least one embodiment, network106 comprises one or more networks, such as Internet, a local areanetwork, a wide area network and/or variations thereof. In at least oneembodiment, different types of network 106 are described with respect toFIG. 18 below. In at least one embodiment, training data 102 comprisesimage data and associated text data, and annotations 104 compriseannotations (e.g., labels) determined using various NLP based annotationtools from associated text data. In at least one embodiment, annotations104 are determined through analysis and processing of text data byvarious language processing algorithms. In at least one embodiment,training data 102 comprises medical images. In at least one embodiment,annotations 104 comprises text reports and/or text data associated withtraining data 102.

In at least one embodiment, GPU 114 processes a mini-batch of trainingdata (e.g., a batch of training data, a subset of training data, or aportion of training data) with model 108. In at least one embodiment,training data 102 comprises images and each image corresponds to a setof annotations of annotations 104, where each annotation of a set ofannotations is a vector. In at least one embodiment, GPU 114 samples amini-batch of training data 102 and determines a corresponding set ofannotations of annotations 104, and mini-batch of training data 102 isrun through model 108 by GPU 114 for each annotation of correspondingset of annotations. In at least one embodiment, model 108 is updatedseparately by GPU 114 for each run of a mini-batch of training data 104based on various loss functions utilizing each annotation of acorresponding set of annotations as ground truth data. In at least oneembodiment, features are calculated by GPU 114 for each annotation of aset of annotations corresponding to a mini-batch of training data 102.In at least one embodiment, weights are generated by GPU 114 forcalculated features. In at least one embodiment, weighted averages arecalculated by GPU 114 from generated weights. In at least oneembodiment, various binarization processes are performed by GPU 114 inconnection with calculated weighted averages.

In at least one embodiment, GPU 114 selects best match annotation 112from annotations 104 for training model 108. In at least one embodiment,weighted averages calculated for each annotation of a set of annotationscorresponding to a mini-batch of training data 102 are analyzed by GPU114 to determine best match annotation 112 to utilize to train model108. In at least one embodiment, a best match annotation is anannotation that is best suited for model 108, which may also be referredto as a most relevant annotation, most reliable annotation, and/orvariations thereof. In at least one embodiment, model 108 is trainedmore accurately utilizing a best match annotation as compared toutilizing other annotations of annotations 104. In at least oneembodiment, model 108 is trained by GPU 114 using best match annotation112 and updated by GPU 114 to generate updated model 110. In at leastone embodiment, model 108 is trained in connection with various lossfunctions by GPU 114 utilizing a mini-batch of data and best matchannotation 112 to generate updated model 110. Further informationregarding training model 108 is found in description of FIG. 2. In atleast one embodiment, updated model 110 is continuously updated by GPU114 through one or more training processes.

FIG. 2 illustrates a diagram 200 of a model and training scheme, inaccordance with at least one embodiment. In at least one embodiment,diagram 200 includes a model Θ 202 that utilizes an input report 212 andinput 214, comprises an embedding generator 204, neural network 206, andan image feature detection 208, and generates a multi-label prediction210. In at least one embodiment, model Θ 202 is trained by one or moresystems in connection with multiple annotations 216. In at least oneembodiment, a system trains model Θ 202 by utilizing multipleannotations 216 to generate features 218, which are utilized to generateweighted averages through weighted average determination 220, which areprocessed in differentiable binarization 222 to determine how to updatemodel Θ 202.

In at least one embodiment, model Θ 202 is a multi-label classificationmodel. In at least one embodiment, a multi-label classification model isa model that classifies one or more aspects and/or features of an input214. In at least one embodiment, model Θ 202 comprises neural network206, image feature detection 208, and embedding generator 204, andgenerates multi-label prediction 210. In at least one embodiment, neuralnetwork 206 comprises one or more neural networks, such as one or moreconvolutional neural networks (CNN), residual neural networks (ResNet),and/or variations thereof. In at least one embodiment, neural network206 comprises one or more neural networks that, based on an input,determine one or more aspects of input. In at least one embodiment,neural network 206 comprises a ResNet with 50 layers.

In at least one embodiment, embedding generator 204 comprises one ormore neural networks that generate text embeddings, such as a word2vecembedding generator, various bidirectional long short-term memory (LSTM)networks, a bidirectional encoder representations from transformers forbiomedical text mining (BioBERT) model, and/or variations thereof. In atleast one embodiment, a text embedding is a vector that represents oneor more aspects of text. In at least one embodiment, a text embeddinggenerator maps words and/or phrases of text to vectors of real numbers.In at least one embodiment, image feature detection 208 comprises one ormore neural networks that detect various features of images. In at leastone embodiment, image feature detection 208 detects one or more featuresof outputs of neural network 206. In at least one embodiment, imagefeature detection 208 comprises one or more global averaging pooling(GAP) layers. In at least one embodiment, image feature detection 208comprises a GAP layer that transforms outputs, which is referred to asactivations, from neural network 206 into one dimensional imagefeatures. In at least one embodiment, one or more image features aredenoted as F_(θ).

In at least one embodiment, data fed to model Θ 202 include input report212 and input 214. In at least one embodiment, input 214 includesdifferent types of inputs (non-limiting examples include: images, video,integers, or characters) and input report 212 is a description of one ormore aspects of image. In at least one embodiment, input 214 is amedical image and input report 212 is a report corresponding to variousaspects of a medical image. In at least one embodiment, input report 212is generated by one or more medical systems in connection with input214. In at least one embodiment, input report 212 is a report by one ormore medical professionals regarding input 214. In at least oneembodiment, input report 212 is generated by a device that executes anNLP algorithm for input 214. In at least one embodiment, different NLPalgorithms for input 214 are executed by different devices. In at leastone embodiment, different NLP algorithms are provided by libraries suchas Apache OpenNLP, Natural Language Toolkit (NLTK), Stanford NLOP,and/or Machine Learning For Language Toolkit (MALLET).

In at least one embodiment, input report 212 and input 214 are processedby model Θ 202 to determine multi-label prediction 210, which comprisesvarious predictions of presences of one or more findings in input 214.In at least one embodiment, input report 212 and input 214 are processedby model Θ 202 to determine one or more features, denoted as F_(θ), ofinput report 212 and/or input 214. In at least one embodiment, input 214is processed by neural network 206 to determine various aspects and/orfeatures of input 214. In at least one embodiment, image featuredetection 208 detects various image features from determined variousaspects and/or features of input 214. In at least one embodiment, inputreport 212 is processed by embedding generator 204 to generate a textembedding corresponding to input report 212. In at least one embodiment,a text embedding corresponding to input report 212 is a 768 dimensionreal-valued vector.

In at least one embodiment, a text embedding corresponding to inputreport 212 and detected image features from input 214 are utilized bymodel Θ 202 to determine multi-label prediction 210. In at least oneembodiment, multi-label prediction 210 comprises a prediction ofpresences of various features of input 214. In at least one embodiment,multi-label prediction 210 is a binary label vector that comprisespredictions of various features of input 214, where each element ofvector corresponds to a specific feature. In at least one embodiment,referring to FIG. 2, multi-label prediction 210 comprises predictions ofvarious features of input 214, such as predictions of presences ofvarious diseases, disease patterns, anomalies, and/or variationsthereof.

In at least one embodiment, model Θ 202 is trained by one or moresystems such as those described in accordance with FIG. 1. In at leastone embodiment, model Θ 202 is trained by a system, such as a processorcomprising one or more circuits, a GPU, and/or variations thereof,utilizing an attention-on-label training scheme. In at least oneembodiment, training data for model Θ 202 comprises various data, datareports, and data labels. In at least one embodiment, training data formodel Θ 202 comprises images (e.g., input 214), corresponding imagereports (e.g., input reports 212), and corresponding image labels thathave been generated from corresponding image reports through one or moreNLP processes. In at least one embodiment, NLP processes are performedby different computing resources (e.g., processors or computing devicesin different hospital locations). In at least one embodiment, trainingdata for model Θ 202 comprises medical images, corresponding medicalimage reports, and corresponding annotations, which is also referred toas labels, generated from corresponding medical image reports. In atleast one embodiment, system trains model Θ 202 using training data thatcomprises a medical image associated with a medical image report with asingle annotation. In at least one embodiment, system then updates modelΘ 202 with a single annotation.

In at least one embodiment, for training data comprising multipleannotations 216, a mini-batch of training data is obtained and utilizedby a system to train model Θ 202. In at least one embodiment, amini-batch of training data comprises a portion of input 214 such asmedical images, and, for each medical image, one or more correspondingmedical image reports comprising multiple annotations 216. In at leastone embodiment, a mini-batch of training data comprising one or moreimages and multiple annotations 216 corresponding to one or more imagesis utilized to train model Θ 202. In at least one embodiment, amini-batch of data from a training set is represented, althoughrepresentations vary, as (X, Y), where X={x₁, . . . , x_(B)} denotes Bsamples, and Y={Y₁, . . . , Y_(m), . . . , Y_(M)} denotes M sets oflabels, in which each Y_(m) is a binary vector with length Ccorresponding to C types of findings.

In at least one embodiment, annotations of multiple annotations 216represent presences of various disease patterns or other findings in aparticular image. In at least one embodiment, each annotation ofmultiple annotations 216 is represented, although representations vary,as a binary label vector Y_(M)=[Y₁, . . . , Y_(n), . . . , Y_(N)],Y_(n)∈{0, 1}, N=14, in which Y_(n)=1 indicates a presence of acorresponding disease pattern or other finding in a particular image,and Y_(n)=0 otherwise. In at least one embodiment, each image oftraining data corresponds to a particular set of multiple annotations216.

In at least one embodiment, for each annotation of multiple annotations216 of a mini-batch of training data, loss is computed by a systemtraining model Θ 202 from inferences of model Θ 202 with mini-batch andperformance gradient descent step to back propagate computed loss toweights of parameters of model Θ 202 to update weights to generate anupdated model {circumflex over (Θ)}. In at least one embodiment, acorresponding image and image report are input to model Θ 202, loss iscalculated by comparing particular annotation with an output multi-labelprediction, and calculated loss is utilized by a system training model Θ202 to select a best annotation for model Θ 202. In at least oneembodiment, best annotation is used to update parameter values of modelΘ 202 to generate an updated model. In at least one embodiment, one ormore loss functions such as a binary cross entropy (BCE) loss functionis utilized by a system training model Θ 202 to compute loss. In atleast one embodiment, other common loss functions such as mean squareerror or mean absolute error are utilized. In at least one embodiment, aBCE loss function, denoted by L_(c)(X,{right arrow over (Y)}) where Xindicates a predicted value and {right arrow over (Y)} indicates apredicted value, is represented by a following equation, although anyvariations are utilized:

${L_{C}\left( {X,\overset{\rightarrow}{Y}} \right)} = {{\sum\limits_{y_{c} = 1}{- {\ln\left( {f\left( x_{c} \right)} \right)}}} + {\sum\limits_{y_{c} = 0}{- {\ln\left( {1 - {f\left( x_{c} \right)}} \right)}}}}$

In at least one embodiment, in connection with a mini-batch of trainingdata comprising one or more images and corresponding multipleannotations 216, for each annotation of multiple annotations 216, BCEloss is computed from inferences of model Θ 202 with mini-batch andperformance gradient descent step to back propagate computed loss toweights of model Θ 202 parameters to obtain new model Θ 202 weightsthrough a following equation, although any variations are utilized:

{circumflex over (Θ)}_(m)=Θ−α∇_(Θ) L _(c)(X,Y _(m),Θ)

where L_(c) denotes binary cross entropy loss, {circumflex over (Θ)}_(m)denotes an updated model, and α denotes a learning rate. In at least oneembodiment, BCE loss (or other commons loss functions) is used as anindicator a comparison between mini-batch

In at least one embodiment, referring to FIG. 2, for each annotation ofmultiple annotations 216, BCE loss is computed by a system trainingmodel Θ 202 and utilized to generate an updated model, denoted by{circumflex over (Θ)}_(M), where M corresponds to a particularannotation of multiple annotations (e.g., {circumflex over (Θ)}₁corresponds to Y₁, {circumflex over (Θ)}_(m) corresponds to Y_(m), andso on). In at least one embodiment, each updated model (e.g.,{circumflex over (Θ)}₁, {circumflex over (Θ)}₂, . . . , {circumflex over(Θ)}_(M)) is utilized by a system training model Θ 202, in connectionwith a mini-batch of training data, to determine sets of features,denoted by F_({circumflex over (Θ)}) ₁ , F_({circumflex over (Θ)}) ₂ , .. . , F_({circumflex over (Θ)}) _(M) . In at least one embodiment, adetermined set of features represent characteristics of correspondinglabel sets/annotations of multiple annotations 216. In at least oneembodiment, referring to FIG. 2, a set of features denoted byF_({circumflex over (Θ)}) ₁ is determined for Y₁ and represents featuresof Y₁, a different set of features denoted by F_({circumflex over (Θ)})₂ is determined for Y₂ and represents features of Y₂, and so on.

In at least one embodiment, determined sets of features are concatenatedtogether by a system training model Θ 202 to form features 218. In atleast one embodiment, features 218 is a vector that comprises sets offeatures determined utilizing updated models for each annotation ofmultiple annotations 216. In at least one embodiment, for example,referring to FIG. 2, features 218 comprises features (e.g.,F_({circumflex over (Θ)}) ₁ , F_({circumflex over (Θ)}) ₂ , . . . ,F_({circumflex over (Θ)}) _(M) ) determined utilizing updated models(e.g., {circumflex over (Θ)}₁, {circumflex over (Θ)}₂, . . . ,{circumflex over (Θ)}_(M)) corresponding to annotations (e.g., Y₁, Y₂, .. . , Y_(M)) of multiple annotations 216.

In at least one embodiment, features 218 are processed by a weightedaverage determination 220 to determine weighted averages correspondingto features 218. In at least one embodiment, a weighted averagedetermination 220 comprises various neural networks, processes, andfunctions that determine weighted averages from features. In at leastone embodiment, weighted average determination 220 is implemented by asystem training model Θ 202. In at least one embodiment, features 218are processed in a weighted average determination 220 by one or morefully-connected layers and activation functions. In at least oneembodiment, a fully-connected layer refers to a layer of a neuralnetwork that connects all inputs to every neuron in a next layer. In atleast one embodiment, an activation function refers to a function thatdefines an output given a set of inputs. In at least one embodiment,features 218 are processed by a system training model Θ 202 via afully-connected layer, a tan h activation function, which is alsoreferred to as a hyperbolic tangent function, and a softmax activationfunction, which is also referred to as a normalized exponentialfunction, to determine weights corresponding to features 218.

In at least one embodiment, weights are determined by weighted averagedetermination 220 through a following equation, although any variationis utilized:

w _(m)=softmax(tan h(FC(Concatenate({F _(m)}))))

where w_(m) denotes a weight, F_(m) denotes a set of features, FCdenotes a fully-connected layer, and softmax and tan h denote activationfunctions. In at least one embodiment, determined weights are thenprocessed by weighted average determination 220 to compute weightedaverages. In at least one embodiment, determined weights indicatemeasures of importance of corresponding label sets/annotations ofmultiple annotations 216. In at least one embodiment, a weighted averageis computed by a system training model Θ 202 for each annotation ofmultiple annotations 216 (e.g., referring to FIG. 2, weighted averagedenoted by w₁ is determined based on features F_({circumflex over (Θ)})₁ from updated model {circumflex over (Θ)}₁ corresponding to Y₁,weighted average denoted by w₂ is determined based on featuresF_({circumflex over (Θ)}) ₂ from updated model {circumflex over (Θ)}₂corresponding to Y₂, and so on).

In at least one embodiment, weighted average values are between 0 and 1.In at least one embodiment, differentiable binarization 222 is utilizedto cast weighted average values to either 0 or 1. In at least oneembodiment, differentiable binarization 222 comprises various functionsand processes that perform one or more binarization functions. In atleast one embodiment, differentiable binarization 222 is implemented bya system training model Θ 202. In at least one embodiment, adifferential binarization function such as following is utilized,although any variation thereof is utilized:

${\overset{\hat{}}{Y}}_{n}^{\prime} = \frac{1}{1 + e^{- {k{({{\overset{\hat{}}{Y}}_{n} - T})}}}}$

where k sets a sharpness of a 0 to 1 cliff, as 50 below, and T is apre-defined threshold to slightly adjust a value range. In at least oneembodiment, T is set to 0.1. In at least one embodiment, followingdifferentiable binarization 222 of weighted average values, anannotation of multiple annotations 216 is determined by a systemtraining model Θ 202 and utilized to update model Θ to result in newmodel {circumflex over (Θ)}_(Ŷ) 224. In at least one embodiment,attended labels/annotations of multiple annotations 216 are produced bya system training model Θ 202 that indicate relevancies oflabels/annotations of multiple annotations 216. In at least oneembodiment, a most relevant annotation of multiple annotations 216 isutilized by a system training model Θ 202 to update model Θ 202 toresult in new model {circumflex over (Θ)}_(Ŷ) 224. In at least oneembodiment, an annotation of multiple annotations 216 that mostaccurately trains model Θ 202 is utilized by a system training model Θ202 to train and update model Θ 202 to result in new model {circumflexover (Θ)}_(Ŷ) 224. In at least one embodiment, an annotation denoted byŶ is utilized by a system training model Θ 202 to update model Θ 202 toresult in new model {circumflex over (Θ)}_(Ŷ) 224. In at least oneembodiment, new model {circumflex over (Θ)}_(Ŷ) 224 is determinedthrough a following equation, although any variation thereof isutilized:

{circumflex over (Θ)}_(Ŷ) ←Θ−β∇L _(c)(X,Ŷ,{right arrow over (Θ)})

where {circumflex over (Θ)}_(Ŷ) denotes a new updated model, and βdenotes a global learning rate.

In at least one embodiment, an algorithm such as following is utilizedby one or more systems to train a model θ, although any variationthereof is utilized:

ALGORITHM Meta-training with attention on labels 1: Randomly initializeθ 2: while not done do 3:  Sample a mini-batch (X, Y) of size B fromtraining data 4:  while m ϵ {1 : M} do 5:     Compute updated parameterswith gradients:     {circumflex over (θ)}_(m) = θ − α∇_(θ) 

_(c)(X, Y_(m), θ) 6:    Compute new features F_(m) using newly updated θ7:  Concatenate features Concatenate (F_(m)) 8:  Compute softmaxattentions to generate weight w_(m)  for each feature F_(m) 9:  Computea weighted average of all sets of labels  for each data sample 10: Perform differentiable binarization for each  data sample (X, Y, {rightarrow over (θ)}) 11:  Update final model {circumflex over(θ)}{circumflex over (_(Y))} ← θ − β∇ 

c(X, Ŷ, {right arrow over (θ)})

FIG. 3 shows an illustrative example of a process 300 to select anannotation to train a neural network, in accordance with at least oneembodiment. In at least one embodiment, some or all of process 300 (orany other processes described herein, or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with computer-executable instructions and is implemented ascode (e.g., computer-executable instructions, one or more computerprograms, or one or more applications) executing collectively on one ormore processors, by hardware, software, or combinations thereof. In atleast one embodiment, code is stored on a computer-readable storagemedium in form of a computer program comprising a plurality ofcomputer-readable instructions executable by one or more processors. Inat least one embodiment, a computer-readable storage medium is anon-transitory computer-readable medium. In at least one embodiment, atleast some computer-readable instructions usable to perform process 300are not stored solely using transitory signals (e.g., a propagatingtransient electric or electromagnetic transmission). A non-transitorycomputer-readable medium does not necessarily include non-transitorydata storage circuitry (e.g., buffers, caches, and queues) withintransceivers of transitory signals. In at least one embodiment, process300 is performed at least in part on a computer system such as thosedescribed elsewhere in this disclosure. In at least one embodiment,process 300 is performed by one or more systems such as those describedin connection with FIG. 1 and FIG. 2. In at least one embodiment, asystem processes annotations and selects an annotation for training of aneural network.

In at least one embodiment, a system performing at least a part ofprocess 300 includes executable code to pass 302 data associated with aplurality of annotations through a neural network. In at least oneembodiment, training data comprising a plurality of annotations isutilized by a system to train a neural network. In at least oneembodiment, a neural network comprises one or more convolutional neuralnetwork (CNN) architectures. In at least one embodiment, a neuralnetwork comprises one or more neural networks that, based on input data,classify one or more aspects of input data. In at least one embodiment,a neural network includes a multi-label classification model. In atleast one embodiment, training data comprises image data, associatedtext data, and a plurality of annotations corresponding to associatedtext data. In at least one embodiment, a plurality of annotations aredetermined by a system through various NLP and/or natural languageinterpretation (NLI) processes. In at least one embodiment, for eachannotation of a plurality of annotations, data of training data is runby a system through a particular neural network.

In at least one embodiment, a system performing at least a part ofprocess 300 includes executable code to compare 304 annotations for animage with output of neural network being trained. In at least oneembodiment, results from passing data through a neural network arecompared by a system with a plurality of annotations. In at least oneembodiment, one or more loss functions, such as a BCE loss function, areutilized by a system. In at least one embodiment, for each run of datathrough a particular neural network, one or more loss functions areutilized by a system to update parameters of particular neural network.In at least one embodiment, for each updated neural network, featuresare calculated by a system utilizing data of training data. In at leastone embodiment, weights are generated by a system for calculatedfeatures. In at least one embodiment, weighted averages are calculatedby a system from generated weights. In at least one embodiment, variousbinarization processes are performed by a system in connection withcalculated weighted averages.

In at least one embodiment, a system performing at least a part ofprocess 300 includes executable code to select 306 best annotation fortraining neural network. In at least one embodiment, weighted averagescalculated for each annotation of a plurality of annotations areanalyzed by a system to determine a best annotation. In at least oneembodiment, a best annotation refers to an annotation of a plurality ofannotations that is utilized to most accurately train one or more neuralnetworks. In at least one embodiment, a neural network is trained moreaccurately utilizing a best annotation as compared to utilizing otherannotations of a plurality of annotations.

In at least one embodiment, a system performing at least a part ofprocess 300 includes executable code to use 308 selected annotation andtraining image to train neural network. In at least one embodiment, aneural network is trained by a system using selected annotation andassociated data of training data. In at least one embodiment, a neuralnetwork is trained by a system in connection with various loss functionsutilizing a selected annotation and training image to generate anupdated neural network. In at least one embodiment, a neural network iscontinuously updated by a system through one or more training processes.In at least one embodiment, one or more processes of process 300 areperformed by a system in any order, including parallel.

FIG. 4 shows an illustrative example of a process 400 to processtraining data and update a model, in accordance with at least oneembodiment. In at least one embodiment, some or all of process 400 (orany other processes described herein, or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with computer-executable instructions and is implemented ascode (e.g., computer-executable instructions, one or more computerprograms, or one or more applications) executing collectively on one ormore processors, by hardware, software, or combinations thereof. Code,in at least one embodiment, is stored on a computer-readable storagemedium in form of a computer program comprising a plurality ofcomputer-readable instructions executable by one or more processors. Acomputer-readable storage medium, in at least one embodiment, is anon-transitory computer-readable medium. In at least one embodiment, atleast some computer-readable instructions usable to perform process 400are not stored solely using transitory signals (e.g., a propagatingtransient electric or electromagnetic transmission). A non-transitorycomputer-readable medium does not necessarily include non-transitorydata storage circuitry (e.g., buffers, caches, and queues) withintransceivers of transitory signals. In at least one embodiment, process400 is performed at least in part on a computer system such as thosedescribed elsewhere in this disclosure. In at least one embodiment,process 400 is performed by one or more systems such as those describedin connection with FIG. 1 and FIG. 2. In at least one embodiment, asystem processes training data and updates a model.

In at least one embodiment, a system performing at least a part ofprocess 400 includes executable code to process 402 mini-batch of datafrom training set. In at least one embodiment, a system with one or moreprocessors train a multi-label classification model is with data from atraining set. In at least one embodiment, a training set comprises imagedata, associated text data, which is also referred to as image reports,and corresponding sets of labels, which is also referred to asannotations, determined from associated text data. In at least oneembodiment, a mini-batch of data from a training set comprises imagedata, associated text data, and a set of labels comprising multiplelabels that correspond to associated text data. In at least oneembodiment, a system samples a mini-batch of data from a training setand determines corresponding label sets. In at least one embodiment, alabel is referred to as a label set.

In at least one embodiment, a system performing at least a part ofprocess 400 includes executable code to, for each label of mini-batch,obtain 404 new model weights. In at least one embodiment, a systemcauses a multi-label classification model to process a mini-batch ofdata. In at least one embodiment, for each label of a set of labels,data of a mini-batch of data is run by a system through a particularmodel. In at least one embodiment, results from passing data throughvarious models are compared by a system with labels of a set of labels.In at least one embodiment, one or more loss functions, such as a BCEloss function, are utilized by a system to compute loss. In at least oneembodiment, for a run of data through a particular model, one or moreloss functions are utilized by a system to determine new model weightsto update parameters of particular model.

In at least one embodiment, a system performing at least a part ofprocess 400 includes executable code to compute 406 a set of newfeatures representing characteristics of corresponding label sets. In atleast one embodiment, for each updated model corresponding to a label ofa set of labels, a set of features is calculated by a system utilizingdata of a mini-batch of data from a training set. In at least oneembodiment, for a set of features computed for a particular label set,set of features represent various characteristics, features, and/oraspects of particular label set.

In at least one embodiment, a system performing at least a part ofprocess 400 includes executable code to use 408 set of new features tocompute a weight for each label from mini-batch. In at least oneembodiment, weights are computed by a system for computed sets of newfeatures. In at least one embodiment, weighted averages are calculatedby a system from computed weights. In at least one embodiment, variousbinarization processes are performed by a system in connection withcalculated weighted averages. Further information regarding calculatingweights is found in description of FIG. 2.

In at least one embodiment, a system performing at least a part ofprocess 400 includes executable code to produce 410 attended labels andupdate model. In at least one embodiment, attended labels representlabels of a set of labels that are most relevant to training a model. Inat least one embodiment, a multi-label classification model is trainedby a system in connection with various loss functions utilizing attendedlabels to produce an updated model. In at least one embodiment, a modelis continuously updated by a system through one or more trainingprocesses. In at least one embodiment, one or more processes of process400 are performed by a system in any order, including parallel.

FIG. 5 illustrates a diagram 500 of a comparison of learning paradigms,in accordance with at least one embodiment. In at least one embodiment,diagram 500 depicts differences between three learning paradigms interms of how gradients are utilized for training. In at least oneembodiment, diagram 500 comprises a gradient based learning method 502,a meta-learning with multiple learning target method 504, and anattention-on-label learning method 506. In at least one embodiment,attention-on-label learning method 506 comprises utilizing meta-trainingwith gradients from various label sets to select a best label for finalgradient back-propagation.

In at least one embodiment, gradient based learning method 502 comprisesutilizing gradient-based learning algorithms to find values ofparameters of a function that minimizes a cost function (e.g., ∇L_(c))in a training of one or more neural networks. In at least oneembodiment, meta-learning with multiple learning target method 504comprises determining multiple updated models (e.g., {circumflex over(Θ)}_(a), {circumflex over (Θ)}_(b), {circumflex over (Θ)}_(c)) based onvarious loss functions (e.g., ∇L_(c) ^(a), ∇L_(c) ^(b), ∇L_(c) ^(c)) ina training of one or more neural networks. In at least one embodiment,attention-on-label learning method 506 comprises determining variousweights (e.g., W_(a), W_(b), W_(c)) in connection with loss computedfrom various label sets (e.g., ∇L_(c)(X, Y_(a)), ∇L_(c)(X,Y_(b)),∇L_(c)(X,Y_(c))) to select a best label (e.g., Ŷ) for final gradientback-propagation in a training of one or more neural networks.

In at least one embodiment, effectiveness of an attention-on-labeltraining scheme is evaluated by a system on various datasets, such as alarge scale dataset with NLP-generated image labels (e.g., Mimic ChestX-ray Database) and a small scale dataset with hand-labeled ground truth(e.g., OpenI dataset). In at least one embodiment, a receiver operatingcharacteristic (ROC) curve is a metric utilized to evaluate performanceof multi-label classification tasks. In at least one embodiment, areaunder curve (AUC) values are a quantitative evaluation metric forcomparison purposes.

In at least one embodiment, following training methods such as residualneural network, confusion matrix, knowledge graph, and text-imageembedding network are utilized in experiments. In at least oneembodiment, an attention-on-label method is denoted as AOL.

In at least one embodiment, a residual neural network with 50 layers isutilized. In at least one embodiment, a pre-trained residual neuralnetwork is utilized as a backbone, followed by a global average poolinglayer and a fully-connected layer for final classification. In at leastone embodiment, a residual neural network with 50 layers is denoted asResNet50.

In at least one embodiment, a confusion matrix method multiplies aconfusion matrix with a probability that a model produces for eachclass. In at least one embodiment, a basic assumption is that thisconfusion matrix corrects missed labeled data and returns probabilitiesfor truth using learned confusion matrices. In at least one embodiment,confusion matrix method is denoted as CM.

In at least one embodiment, knowledge graph method utilizes priorknowledge of disease relations as a form of a knowledge graph. In atleast one embodiment, by injecting such prior knowledge and employinggraph convolutional network, knowledge graph method learns underlyinginfo for final classification and report generation task. In at leastone embodiment, knowledge graph method is denoted as NG.

In at least one embodiment, a text-image embedding network determineshow to learn image and text embeddings together using a convolutionalneural network and a recurrent neural network framework. In at least oneembodiment, a dedicated heavy long short-term memory (LSTM) basednetwork is utilized to learn and encode text reports. In at least oneembodiment, a text-image embedding network is denoted as TieNet.

In at least one embodiment, for pre-processing, images are resized to256×256 and normalized to [0, 1]. In at least one embodiment, no dataaugmentation is employed in experiments. In at least one embodiment, alearning rate for a meta-training phase is set as =0.2 and a globallearning rate is set as =0.2. In at least one embodiment, a best modelfor all hyper-parameters is determined via validation. In at least oneembodiment, a GPU such as a Titan-X Pascal GPU is utilized for trainingclassification models. In at least one embodiment, a batch size B=32 isutilized.

In at least one embodiment, experiments compare benefits of utilizingdifferent optimizers for a proposed learning process. In at least oneembodiment, a stochastic gradient descent (SGD) with a momentum of 0.9and a weight decay of 104 is applied. In at least one embodiment, for anadaptive learning rate optimization algorithm such as an Adam optimizer,gradient clipping at 5.0 is performed. In at least one embodiment,results are generated using Adam optimizer.

In at least one embodiment, large differences are observed amongalgorithm generated label sets. In at least one embodiment, a baselineis set up to see how different label sets will affect model training. Inat least one embodiment, following table illustrates averaged AUCs forfour different label sets:

Label Label Label Label Baseline Model Set 1 Set 2 Set 3 Set 4 AverageAUC 0.825 0.821 0.824 0.810 (Large Dataset) Average AUC 0.751 0.7560.755 0.752 (Small Dataset)

In at least one embodiment, in connection with a labelling algorithmsuch as a NegBio algorithm, label set 1 is generated by setting alluncertain cases to 0, and label set 2 is generated by setting alluncertain cases to 1, and label set 3 and label set 4 are generated in asimilar manner in connection with a label set such as a CheXpert labelset. In at least one embodiment, testing performance of all four labelsets are relatively on a same level for both a large dataset (e.g.,Mimic-CXR) and a small dataset (e.g., OpenI). In at least oneembodiment, results indicate that a CNN based model is not so sensitiveto a change of labels and overcomes noise in a label set to a certaindegree, but does not necessarily improve an overall performance of atrained model. In at least one embodiment, a large amount of data withhigher quality labels benefits training and makes a trained model moreaccurate and robust.

In at least one embodiment, following table shows evaluation results forall compared methods using only images as input to a model:

Image Only Large Scale Dataset Small Scale Dataset Disease ResNet50 CMAOL NG TieNet ResNet50 CM AOL Atelectasis 0.821 0.832 0.826 0.833 0.7740.781 0.81 0.826 Cardiomegaly 0.825 0.852 0.879 0.913 0.847 0.859 0.8810.879 Consolidation 0.762 0.751 0.906 — — 0.829 0.842 0.906 Edema 0.8870.903 0.885 0.931 0.879 0.895 0.924 0.885 E-cardio 0.74 0.757 0.725 — —0.795 0.758 0.725 Fracture 0.722 0.771 0.632 0.671 — 0.513 0.596 0.632Lung-lesion 0.765 0.744 0.643 0.643 0.658 0.585 0.58 0.643 Lung-opacity0.814 0.82 0.775 0.803 — 0.742 0.738 0.775 No-finding 0.857 0.863 0.775— 0.747 0.754 0.739 0.775 Effusion 0.906 0.914 0.942 0.942 0.899 0.9120.932 0.942 Pleural-other 0.866 0.829 0.705 — — 0.648 0.676 0.705Pneumonia 0.809 0.809 0.871 0.863 0.731 0.781 0.823 0.871 Pneum-x 0.8660.858 0.833 0.843 0.709 0.793 0.882 0.833 Devices 0.92 0.926 0.729 0.805— 0.628 0.655 0.729 Average 0.825 0.830 0.794 — — 0.751 0.774 0.794

In at least one embodiment, left side of above table shows AUCs of allfinding categories from ResNet, CM, and AOL. In at least one embodiment,averaged AUC for AOL drops from a baseline. In at least one embodiment,considering that testing set of large scale dataset (e.g., Mimic-CXRdataset) is also using algorithm based labels, AOL predictions divergefrom those noisy labels and lean to underlying true labels, as it isproved by results illustrated in small scale dataset (e.g., OpenIdataset) section (right part of above table). In at least oneembodiment, small scale dataset (e.g., OpenI dataset) has hand-labeledground truth and AOL is able to achieve over 4% increase in averagedAUC, which is also greater than what a CM method achieves.

In at least one embodiment, uncertainty of label sets is a source oflearning true labels. In at least one embodiment, those diseasecategories with larger amount of uncertainties gain more from anattention on label meta-training process, e.g., atelectasis andconsolidation. In at least one embodiment, following table shows resultsfor an image-text classification task:

Image-Text Large Scale Dataset Small Scale Dataset Disease ResNet50 CMAOL TieNet ResNet50 CM AOL Atelectasis 0.985 0.986 0.981 0.976 0.9010.909 0.925 Cardiomegaly 0.946 0.953 0.949 0.962 0.915 0.928 0.949Consolidation 0.911 0.913 0.904 — 0.914 0.891 0.907 Edema 0.955 0.9520.956 0.995 0.903 0.915 0.939 E-cardio 0.923 0.916 0.936 — 0.581 0.7140.598 Fracture 0.935 0.766 0.876 — 0.705 0.683 0.739 Lung-lesion 0.8650.885 0.814 0.96 0.615 0.607 0.649 Lung-opacity 0.969 0.968 0.967 —0.849 0.854 0.877 No-finding 0.975 0.972 0.968 0.936 0.79 0.82 0.867Effusion 0.974 0.973 0.974 0.977 0.944 0.948 0.943 Pleural-other 0.920.877 0.892 — 0.723 0.778 0.739 Pneumonia 0.927 0.931 0.933 0.994 0.8120.834 0.889 Pneum-x 0.929 0.919 0.926 0.96 0.879 0.879 0.853 Devices0.971 0.969 0.97 — 0.796 0.787 0.821 Average 0.941 0.927 0.931 — 0.8090.824 0.835

In at least one embodiment, similar results are observed on animage-text classification task. In at least one embodiment, increase ofoverall AUCs occurs. In at least one embodiment, an attention-on-labelscheme boosts classification performance with a significant margin. Inat least one embodiment, TieNet achieves better classification resultsin some categories due to utilization of a complex text embeddingnetwork. In at least one embodiment, more accurate results are obtainedwhen utilizing an attention-on-label scheme in connection with a complextext embedding network. In at least one embodiment, anattention-on-label scheme is also applicable to other learningframeworks and applications such as LSTM based networks andvision-language tasks.

Inference and Training Logic

FIG. 6A illustrates inference and/or training logic 615 used to performinferencing and/or training operations associated with one or moreembodiments. Details regarding inference and/or training logic 615 areprovided below in conjunction with FIGS. 6A and/or 6B.

In at least one embodiment, inference and/or training logic 615 mayinclude, without limitation, code and/or data storage 601 to storeforward and/or output weight and/or input/output data, and/or otherparameters to configure neurons or layers of a neural network trainedand/or used for inferencing in aspects of one or more embodiments. In atleast one embodiment, training logic 615 may include, or be coupled tocode and/or data storage 601 to store graph code or other software tocontrol timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs). In at least one embodiment, code, such as graph code, loadsweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which such code corresponds. In atleast one embodiment, code and/or data storage 601 stores weightparameters and/or input/output data of each layer of a neural networktrained or used in conjunction with one or more embodiments duringforward propagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, any portion of code and/or data storage 601 maybe included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 601may be internal or external to one or more processors or other hardwarelogic devices or circuits. In at least one embodiment, code and/or codeand/or data storage 601 may be cache memory, dynamic randomlyaddressable memory (“DRAM”), static randomly addressable memory(“SRAM”), non-volatile memory (e.g., flash memory), or other storage. Inat least one embodiment, a choice of whether code and/or code and/ordata storage 601 is internal or external to a processor, for example, orcomprising DRAM, SRAM, flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors.

In at least one embodiment, inference and/or training logic 615 mayinclude, without limitation, a code and/or data storage 605 to storebackward and/or output weight and/or input/output data corresponding toneurons or layers of a neural network trained and/or used forinferencing in aspects of one or more embodiments. In at least oneembodiment, code and/or data storage 605 stores weight parameters and/orinput/output data of each layer of a neural network trained or used inconjunction with one or more embodiments during backward propagation ofinput/output data and/or weight parameters during training and/orinferencing using aspects of one or more embodiments. In at least oneembodiment, training logic 615 may include, or be coupled to code and/ordata storage 605 to store graph code or other software to control timingand/or order, in which weight and/or other parameter information is tobe loaded to configure, logic, including integer and/or floating pointunits (collectively, arithmetic logic units (ALUs).

In at least one embodiment, code, such as graph code, causes the loadingof weight or other parameter information into processor ALUs based on anarchitecture of a neural network to which such code corresponds. In atleast one embodiment, any portion of code and/or data storage 605 may beincluded with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory. In at least oneembodiment, any portion of code and/or data storage 605 may be internalor external to one or more processors or other hardware logic devices orcircuits. In at least one embodiment, code and/or data storage 605 maybe cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory),or other storage. In at least one embodiment, a choice of whether codeand/or data storage 605 is internal or external to a processor, forexample, or comprising DRAM, SRAM, flash memory or some other storagetype may depend on available storage on-chip versus off-chip, latencyrequirements of training and/or inferencing functions being performed,batch size of data used in inferencing and/or training of a neuralnetwork, or some combination of these factors.

In at least one embodiment, code and/or data storage 601 and code and/ordata storage 605 may be separate storage structures. In at least oneembodiment, code and/or data storage 601 and code and/or data storage605 may be a combined storage structure. In at least one embodiment,code and/or data storage 601 and code and/or data storage 605 may bepartially combined and partially separate. In at least one embodiment,any portion of code and/or data storage 601 and code and/or data storage605 may be included with other on-chip or off-chip data storage,including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, inference and/or training logic 615 mayinclude, without limitation, one or more arithmetic logic unit(s)(“ALU(s)”) 610, including integer and/or floating point units, toperform logical and/or mathematical operations based, at least in parton, or indicated by, training and/or inference code (e.g., graph code),a result of which may produce activations (e.g., output values fromlayers or neurons within a neural network) stored in an activationstorage 620 that are functions of input/output and/or weight parameterdata stored in code and/or data storage 601 and/or code and/or datastorage 605. In at least one embodiment, activations stored inactivation storage 620 are generated according to linear algebraic andor matrix-based mathematics performed by ALU(s) 610 in response toperforming instructions or other code, wherein weight values stored incode and/or data storage 605 and/or data storage 601 are used asoperands along with other values, such as bias values, gradientinformation, momentum values, or other parameters or hyperparameters,any or all of which may be stored in code and/or data storage 605 orcode and/or data storage 601 or another storage on or off-chip.

In at least one embodiment, ALU(s) 610 are included within one or moreprocessors or other hardware logic devices or circuits, whereas inanother embodiment, ALU(s) 610 may be external to a processor or otherhardware logic device or circuit that uses them (e.g., a co-processor).In at least one embodiment, ALUs 610 may be included within aprocessor's execution units or otherwise within a bank of ALUsaccessible by a processor's execution units either within same processoror distributed between different processors of different types (e.g.,central processing units, graphics processing units, fixed functionunits, etc.). In at least one embodiment, code and/or data storage 601,code and/or data storage 605, and activation storage 620 may share aprocessor or other hardware logic device or circuit, whereas in anotherembodiment, they may be in different processors or other hardware logicdevices or circuits, or some combination of same and differentprocessors or other hardware logic devices or circuits. In at least oneembodiment, any portion of activation storage 620 may be included withother on-chip or off-chip data storage, including a processor's L1, L2,or L3 cache or system memory. Furthermore, inferencing and/or trainingcode may be stored with other code accessible to a processor or otherhardware logic or circuit and fetched and/or processed using aprocessor's fetch, decode, scheduling, execution, retirement and/orother logical circuits.

In at least one embodiment, activation storage 620 may be cache memory,DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage.In at least one embodiment, activation storage 620 may be completely orpartially within or external to one or more processors or other logicalcircuits. In at least one embodiment, a choice of whether activationstorage 620 is internal or external to a processor, for example, orcomprising DRAM, SRAM, flash memory or some other storage type maydepend on available storage on-chip versus off-chip, latencyrequirements of training and/or inferencing functions being performed,batch size of data used in inferencing and/or training of a neuralnetwork, or some combination of these factors.

In at least one embodiment, inference and/or training logic 615illustrated in FIG. 6A may be used in conjunction with anapplication-specific integrated circuit (“ASIC”), such as a TensorFlow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 615illustrated in FIG. 6A may be used in conjunction with centralprocessing unit (“CPU”) hardware, graphics processing unit (“GPU”)hardware or other hardware, such as field programmable gate arrays(“FPGAs”).

FIG. 6B illustrates inference and/or training logic 615, according to atleast one embodiment. In at least one embodiment, inference and/ortraining logic 615 may include, without limitation, hardware logic inwhich computational resources are dedicated or otherwise exclusivelyused in conjunction with weight values or other informationcorresponding to one or more layers of neurons within a neural network.In at least one embodiment, inference and/or training logic 615illustrated in FIG. 6B may be used in conjunction with anapplication-specific integrated circuit (ASIC), such as TensorFlow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 615illustrated in FIG. 6B may be used in conjunction with centralprocessing unit (CPU) hardware, graphics processing unit (GPU) hardwareor other hardware, such as field programmable gate arrays (FPGAs). In atleast one embodiment, inference and/or training logic 615 includes,without limitation, code and/or data storage 601 and code and/or datastorage 605, which may be used to store code (e.g., graph code), weightvalues and/or other information, including bias values, gradientinformation, momentum values, and/or other parameter or hyperparameterinformation. In at least one embodiment illustrated in FIG. 6B, each ofcode and/or data storage 601 and code and/or data storage 605 isassociated with a dedicated computational resource, such ascomputational hardware 602 and computational hardware 606, respectively.In at least one embodiment, each of computational hardware 602 andcomputational hardware 606 comprises one or more ALUs that performmathematical functions, such as linear algebraic functions, only oninformation stored in code and/or data storage 601 and code and/or datastorage 605, respectively, result of which is stored in activationstorage 620.

In at least one embodiment, each of code and/or data storage 601 and 605and corresponding computational hardware 602 and 606, respectively,correspond to different layers of a neural network, such that resultingactivation from one storage/computational pair 601/602 of code and/ordata storage 601 and computational hardware 602 is provided as an inputto a next storage/computational pair 605/606 of code and/or data storage605 and computational hardware 606, in order to mirror a conceptualorganization of a neural network. In at least one embodiment, each ofstorage/computational pairs 601/602 and 605/606 may correspond to morethan one neural network layer. In at least one embodiment, additionalstorage/computation pairs (not shown) subsequent to or in parallel withstorage/computation pairs 601/602 and 605/606 may be included ininference and/or training logic 615.

In at least one embodiment, one or more systems depicted in FIG. 6A-FIG.6B are utilized to implement an attention-on-label training process. Inat least one embodiment, one or more systems depicted in FIG. 6A-FIG. 6Bare utilized to implement one or more networks and training schemes suchas those described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 6A-FIG. 6B are utilizedto implement a learning method that utilizes meta-training withgradients from various label sets of training data to select a label forfinal gradient back-propagation.

Neural Network Training and Deployment

FIG. 7 illustrates training and deployment of a deep neural network,according to at least one embodiment. In at least one embodiment,untrained neural network 706 is trained using a training dataset 702. Inat least one embodiment, training framework 704 is a PyTorch framework,whereas in other embodiments, training framework 704 is a TensorFlow,Boost, Caffe, Microsoft Cognitive Toolkit/CNTK, MXNet, Chainer, Keras,Deeplearning4j, or other training framework. In at least one embodiment,training framework 704 trains an untrained neural network 706 andenables it to be trained using processing resources described herein togenerate a trained neural network 708. In at least one embodiment,weights may be chosen randomly or by pre-training using a deep beliefnetwork. In at least one embodiment, training may be performed in eithera supervised, partially supervised, or unsupervised manner.

In at least one embodiment, untrained neural network 706 is trainedusing supervised learning, wherein training dataset 702 includes aninput paired with a desired output for an input, or where trainingdataset 702 includes input having a known output and an output of neuralnetwork 706 is manually graded. In at least one embodiment, untrainedneural network 706 is trained in a supervised manner and processesinputs from training dataset 702 and compares resulting outputs againsta set of expected or desired outputs. In at least one embodiment, errorsare then propagated back through untrained neural network 706. In atleast one embodiment, training framework 704 adjusts weights thatcontrol untrained neural network 706. In at least one embodiment,training framework 704 includes tools to monitor how well untrainedneural network 706 is converging towards a model, such as trained neuralnetwork 708, suitable to generating correct answers, such as in result714, based on input data such as a new dataset 712. In at least oneembodiment, training framework 704 trains untrained neural network 706repeatedly while adjust weights to refine an output of untrained neuralnetwork 706 using a loss function and adjustment algorithm, such asstochastic gradient descent. In at least one embodiment, trainingframework 704 trains untrained neural network 706 until untrained neuralnetwork 706 achieves a desired accuracy. In at least one embodiment,trained neural network 708 can then be deployed to implement any numberof machine learning operations.

In at least one embodiment, untrained neural network 706 is trainedusing unsupervised learning, wherein untrained neural network 706attempts to train itself using unlabeled data. In at least oneembodiment, unsupervised learning training dataset 702 will includeinput data without any associated output data or “ground truth” data. Inat least one embodiment, untrained neural network 706 can learngroupings within training dataset 702 and can determine how individualinputs are related to untrained dataset 702. In at least one embodiment,unsupervised training can be used to generate a self-organizing map intrained neural network 708 capable of performing operations useful inreducing dimensionality of new dataset 712. In at least one embodiment,unsupervised training can also be used to perform anomaly detection,which allows identification of data points in new dataset 712 thatdeviate from normal patterns of new dataset 712.

In at least one embodiment, semi-supervised learning may be used, whichis a technique in which in training dataset 702 includes a mix oflabeled and unlabeled data. In at least one embodiment, trainingframework 704 may be used to perform incremental learning, such asthrough transferred learning techniques. In at least one embodiment,incremental learning enables trained neural network 708 to adapt to newdataset 712 without forgetting knowledge instilled within trained neuralnetwork 708 during initial training.

In at least one embodiment, one or more systems depicted in FIG. 7 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 7 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 7 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

Data Center

FIG. 8 illustrates an example data center 800, in which at least oneembodiment may be used. In at least one embodiment, data center 800includes a data center infrastructure layer 810, a framework layer 820,a software layer 830 and an application layer 840.

In at least one embodiment, as shown in FIG. 8, data centerinfrastructure layer 810 may include a resource orchestrator 812,grouped computing resources 814, and node computing resources (“nodeC.R.s”) 816(1)-816(N), where “N” represents a positive integer (whichmay be a different integer “N” than used in other figures). In at leastone embodiment, node C.R.s 816(1)-816(N) may include, but are notlimited to, any number of central processing units (“CPUs”) or otherprocessors (including accelerators, field programmable gate arrays(FPGAs), graphics processors, etc.), memory storage devices818(1)-818(N) (e.g., dynamic read-only memory, solid state storage ordisk drives), network input/output (“NW I/O”) devices, network switches,virtual machines (“VMs”), power modules, and cooling modules, etc. In atleast one embodiment, one or more node C.R.s from among node C.R.s816(1)-816(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in data centers at various geographicallocations (also not shown). In at least one embodiment, separategroupings of node C.R.s within grouped computing resources 814 mayinclude grouped compute, network, memory or storage resources that maybe configured or allocated to support one or more workloads. In at leastone embodiment, several node C.R.s including CPUs or processors maygrouped within one or more racks to provide compute resources to supportone or more workloads. In at least one embodiment, one or more racks mayalso include any number of power modules, cooling modules, and networkswitches, in any combination.

In at least one embodiment, resource orchestrator 812 may configure orotherwise control one or more node C.R.s 816(1)-816(N) and/or groupedcomputing resources 814. In at least one embodiment, resourceorchestrator 812 may include a software design infrastructure (“SDI”)management entity for data center 800. In at least one embodiment,resource orchestrator 612 may include hardware, software or somecombination thereof.

In at least one embodiment, as shown in FIG. 8, framework layer 820includes a job scheduler 822, a configuration manager 824, a resourcemanager 826 and a distributed file system 828. In at least oneembodiment, framework layer 820 may include a framework to supportsoftware 832 of software layer 830 and/or one or more application(s) 842of application layer 840. In at least one embodiment, software 832 orapplication(s) 842 may respectively include web-based service softwareor applications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, framework layer820 may be, but is not limited to, a type of free and open-sourcesoftware web application framework such as Apache Spark™ (hereinafter“Spark”) that may utilize distributed file system 828 for large-scaledata processing (e.g., “big data”). In at least one embodiment, jobscheduler 832 may include a Spark driver to facilitate scheduling ofworkloads supported by various layers of data center 800. In at leastone embodiment, configuration manager 824 may be capable of configuringdifferent layers such as software layer 830 and framework layer 820including Spark and distributed file system 828 for supportinglarge-scale data processing. In at least one embodiment, resourcemanager 826 may be capable of managing clustered or grouped computingresources mapped to or allocated for support of distributed file system828 and job scheduler 822. In at least one embodiment, clustered orgrouped computing resources may include grouped computing resources 814at data center infrastructure layer 810. In at least one embodiment,resource manager 826 may coordinate with resource orchestrator 812 tomanage these mapped or allocated computing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 828 of framework layer 820. In at least one embodiment, one ormore types of software may include, but are not limited to, Internet webpage search software, e-mail virus scan software, database software, andstreaming video content software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 828 of framework layer 820. In at leastone embodiment, one or more types of applications may include, but arenot limited to, any number of a genomics application, a cognitivecompute, application and a machine learning application, includingtraining or inferencing software, machine learning framework software(e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learningapplications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager 824, resourcemanager 826, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a data center operator ofdata center 800 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adata center.

In at least one embodiment, data center 800 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments described herein. Forexample, in at least one embodiment, a machine learning model may betrained by calculating weight parameters according to a neural networkarchitecture using software and computing resources described above withrespect to data center 800. In at least one embodiment, trained machinelearning models corresponding to one or more neural networks may be usedto infer or predict information using resources described above withrespect to data center 800 by using weight parameters calculated throughone or more training techniques described herein.

In at least one embodiment, data center may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training and/or inferencing using above-describedresources. Moreover, one or more software and/or hardware resourcesdescribed above may be configured as a service to allow users to trainor performing inferencing of information, such as image recognition,speech recognition, or other artificial intelligence services.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 8 for inferencing or predicting operations based, at least in part,on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 8 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 8 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 8 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

Autonomous Vehicle

FIG. 9A illustrates an example of an autonomous vehicle 900, accordingto at least one embodiment. In at least one embodiment, autonomousvehicle 900 (alternatively referred to herein as “vehicle 900”) may be,without limitation, a passenger vehicle, such as a car, a truck, a bus,and/or another type of vehicle that accommodates one or more passengers.In at least one embodiment, vehicle 900 may be a semi-tractor-trailertruck used for hauling cargo. In at least one embodiment, vehicle 900may be an airplane, robotic vehicle, or other kind of vehicle.

Autonomous vehicles may be described in terms of automation levels,defined by National Highway Traffic Safety Administration (“NHTSA”), adivision of US Department of Transportation, and Society of AutomotiveEngineers (“SAE”) “Taxonomy and Definitions for Terms Related to DrivingAutomation Systems for On-Road Motor Vehicles” (e.g., Standard No.J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609,published on Sep. 30, 2016, and previous and future versions of thisstandard). In at least one embodiment, vehicle 900 may be capable offunctionality in accordance with one or more of Level 1 through Level 5of autonomous driving levels. For example, in at least one embodiment,vehicle 900 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending onembodiment.

In at least one embodiment, vehicle 900 may include, without limitation,components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8,18, etc.), tires, axles, and other components of a vehicle. In at leastone embodiment, vehicle 900 may include, without limitation, apropulsion system 950, such as an internal combustion engine, hybridelectric power plant, an all-electric engine, and/or another propulsionsystem type. In at least one embodiment, propulsion system 950 may beconnected to a drive train of vehicle 900, which may include, withoutlimitation, a transmission, to enable propulsion of vehicle 900. In atleast one embodiment, propulsion system 950 may be controlled inresponse to receiving signals from a throttle/accelerator(s) 952.

In at least one embodiment, a steering system 954, which may include,without limitation, a steering wheel, is used to steer vehicle 900(e.g., along a desired path or route) when propulsion system 950 isoperating (e.g., when vehicle 900 is in motion). In at least oneembodiment, steering system 954 may receive signals from steeringactuator(s) 956. In at least one embodiment, a steering wheel may beoptional for full automation (Level 5) functionality. In at least oneembodiment, a brake sensor system 946 may be used to operate vehiclebrakes in response to receiving signals from brake actuator(s) 948and/or brake sensors.

In at least one embodiment, controller(s) 936, which may include,without limitation, one or more system on chips (“SoCs”) (not shown inFIG. 9A) and/or graphics processing unit(s) (“GPU(s)”), provide signals(e.g., representative of commands) to one or more components and/orsystems of vehicle 900. For instance, in at least one embodiment,controller(s) 936 may send signals to operate vehicle brakes via brakeactuator(s) 948, to operate steering system 954 via steering actuator(s)956, to operate propulsion system 950 via throttle/accelerator(s) 952.In at least one embodiment, controller(s) 936 may include one or moreonboard (e.g., integrated) computing devices that process sensorsignals, and output operation commands (e.g., signals representingcommands) to enable autonomous driving and/or to assist a human driverin driving vehicle 900. In at least one embodiment, controller(s) 936may include a first controller for autonomous driving functions, asecond controller for functional safety functions, a third controllerfor artificial intelligence functionality (e.g., computer vision), afourth controller for infotainment functionality, a fifth controller forredundancy in emergency conditions, and/or other controllers. In atleast one embodiment, a single controller may handle two or more ofabove functionalities, two or more controllers may handle a singlefunctionality, and/or any combination thereof.

In at least one embodiment, controller(s) 936 provide signals forcontrolling one or more components and/or systems of vehicle 900 inresponse to sensor data received from one or more sensors (e.g., sensorinputs). In at least one embodiment, sensor data may be received from,for example and without limitation, global navigation satellite systems(“GNSS”) sensor(s) 958 (e.g., Global Positioning System sensor(s)),RADAR sensor(s) 960, ultrasonic sensor(s) 962, LIDAR sensor(s) 964,inertial measurement unit (“IMU”) sensor(s) 966 (e.g., accelerometer(s),gyroscope(s), a magnetic compass or magnetic compasses, magnetometer(s),etc.), microphone(s) 996, stereo camera(s) 968, wide-view camera(s) 970(e.g., fisheye cameras), infrared camera(s) 972, surround camera(s) 974(e.g., 360 degree cameras), long-range cameras (not shown in FIG. 9A),mid-range camera(s) (not shown in FIG. 9A), speed sensor(s) 944 (e.g.,for measuring speed of vehicle 900), vibration sensor(s) 942, steeringsensor(s) 940, brake sensor(s) (e.g., as part of brake sensor system946), and/or other sensor types.

In at least one embodiment, one or more of controller(s) 936 may receiveinputs (e.g., represented by input data) from an instrument cluster 932of vehicle 900 and provide outputs (e.g., represented by output data,display data, etc.) via a human-machine interface (“HMI”) display 934,an audible annunciator, a loudspeaker, and/or via other components ofvehicle 900. In at least one embodiment, outputs may include informationsuch as vehicle velocity, speed, time, map data (e.g., a High Definitionmap (not shown in FIG. 9A), location data (e.g., vehicle's 900 location,such as on a map), direction, location of other vehicles (e.g., anoccupancy grid), information about objects and status of objects asperceived by controller(s) 936, etc. For example, in at least oneembodiment, HMI display 934 may display information about presence ofone or more objects (e.g., a street sign, caution sign, traffic lightchanging, etc.), and/or information about driving maneuvers vehicle hasmade, is making, or will make (e.g., changing lanes now, taking exit 34Bin two miles, etc.).

In at least one embodiment, vehicle 900 further includes a networkinterface 924 which may use wireless antenna(s) 926 and/or modem(s) tocommunicate over one or more networks. For example, in at least oneembodiment, network interface 924 may be capable of communication overLong-Term Evolution (“LTE”), Wideband Code Division Multiple Access(“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), GlobalSystem for Mobile communication (“GSM”), IMT-CDMA Multi-Carrier(“CDMA2000”) networks, etc. In at least one embodiment, wirelessantenna(s) 926 may also enable communication between objects inenvironment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth Low Energy (“LE”), Z-Wave,ZigBee, etc., and/or low power wide-area network(s) (“LPWANs”), such asLoRaWAN, SigFox, etc. protocols.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 9A for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

FIG. 9B illustrates an example of camera locations and fields of viewfor autonomous vehicle 900 of FIG. 9A, according to at least oneembodiment. In at least one embodiment, cameras and respective fields ofview are one example embodiment and are not intended to be limiting. Forinstance, in at least one embodiment, additional and/or alternativecameras may be included and/or cameras may be located at differentlocations on vehicle 900.

In at least one embodiment, camera types for cameras may include, butare not limited to, digital cameras that may be adapted for use withcomponents and/or systems of vehicle 900. In at least one embodiment,camera(s) may operate at automotive safety integrity level (“ASIL”) Band/or at another ASIL. In at least one embodiment, camera types may becapable of any image capture rate, such as 60 frames per second (fps),1220 fps, 240 fps, etc., depending on embodiment. In at least oneembodiment, cameras may be capable of using rolling shutters, globalshutters, another type of shutter, or a combination thereof. In at leastone embodiment, color filter array may include a red clear clear clear(“RCCC”) color filter array, a red clear clear blue (“RCCB”) colorfilter array, a red blue green clear (“RBGC”) color filter array, aFoveon X3 color filter array, a Bayer sensors (“RGGB”) color filterarray, a monochrome sensor color filter array, and/or another type ofcolor filter array. In at least one embodiment, clear pixel cameras,such as cameras with an RCCC, an RCCB, and/or an RBGC color filterarray, may be used in an effort to increase light sensitivity.

In at least one embodiment, one or more of camera(s) may be used toperform advanced driver assistance systems (“ADAS”) functions (e.g., aspart of a redundant or fail-safe design). For example, in at least oneembodiment, a Multi-Function Mono Camera may be installed to providefunctions including lane departure warning, traffic sign assist andintelligent headlamp control. In at least one embodiment, one or more ofcamera(s) (e.g., all cameras) may record and provide image data (e.g.,video) simultaneously.

In at least one embodiment, one or more camera may be mounted in amounting assembly, such as a custom designed (three-dimensional (“3D”)printed) assembly, in order to cut out stray light and reflections fromwithin vehicle 900 (e.g., reflections from dashboard reflected inwindshield mirrors) which may interfere with camera image data captureabilities. With reference to wing-mirror mounting assemblies, in atleast one embodiment, wing-mirror assemblies may be custom 3D printed sothat a camera mounting plate matches a shape of a wing-mirror. In atleast one embodiment, camera(s) may be integrated into wing-mirrors. Inat least one embodiment, for side-view cameras, camera(s) may also beintegrated within four pillars at each corner of a cabin.

In at least one embodiment, cameras with a field of view that includeportions of an environment in front of vehicle 900 (e.g., front-facingcameras) may be used for surround view, to help identify forward facingpaths and obstacles, as well as aid in, with help of one or more ofcontroller(s) 936 and/or control SoCs, providing information critical togenerating an occupancy grid and/or determining preferred vehicle paths.In at least one embodiment, front-facing cameras may be used to performmany similar ADAS functions as LIDAR, including, without limitation,emergency braking, pedestrian detection, and collision avoidance. In atleast one embodiment, front-facing cameras may also be used for ADASfunctions and systems including, without limitation, Lane DepartureWarnings (“LDW”), Autonomous Cruise Control (“ACC”), and/or otherfunctions such as traffic sign recognition.

In at least one embodiment, a variety of cameras may be used in afront-facing configuration, including, for example, a monocular cameraplatform that includes a CMOS (“complementary metal oxidesemiconductor”) color imager. In at least one embodiment, a wide-viewcamera 970 may be used to perceive objects coming into view from aperiphery (e.g., pedestrians, crossing traffic or bicycles). Althoughonly one wide-view camera 970 is illustrated in FIG. 9B, in otherembodiments, there may be any number (including zero) wide-view camerason vehicle 900. In at least one embodiment, any number of long-rangecamera(s) 998 (e.g., a long-view stereo camera pair) may be used fordepth-based object detection, especially for objects for which a neuralnetwork has not yet been trained. In at least one embodiment, long-rangecamera(s) 998 may also be used for object detection and classification,as well as basic object tracking.

In at least one embodiment, any number of stereo camera(s) 968 may alsobe included in a front-facing configuration. In at least one embodiment,one or more of stereo camera(s) 968 may include an integrated controlunit comprising a scalable processing unit, which may provide aprogrammable logic (“FPGA”) and a multi-core micro-processor with anintegrated Controller Area Network (“CAN”) or Ethernet interface on asingle chip. In at least one embodiment, such a unit may be used togenerate a 3D map of an environment of vehicle 900, including a distanceestimate for all points in an image. In at least one embodiment, one ormore of stereo camera(s) 968 may include, without limitation, compactstereo vision sensor(s) that may include, without limitation, two cameralenses (one each on left and right) and an image processing chip thatmay measure distance from vehicle 900 to target object and use generatedinformation (e.g., metadata) to activate autonomous emergency brakingand lane departure warning functions. In at least one embodiment, othertypes of stereo camera(s) 968 may be used in addition to, oralternatively from, those described herein.

In at least one embodiment, cameras with a field of view that includeportions of environment to sides of vehicle 900 (e.g., side-viewcameras) may be used for surround view, providing information used tocreate and update an occupancy grid, as well as to generate side impactcollision warnings. For example, in at least one embodiment, surroundcamera(s) 974 (e.g., four surround cameras as illustrated in FIG. 9B)could be positioned on vehicle 900. In at least one embodiment, surroundcamera(s) 974 may include, without limitation, any number andcombination of wide-view cameras, fisheye camera(s), 360 degreecamera(s), and/or similar cameras. For instance, in at least oneembodiment, four fisheye cameras may be positioned on a front, a rear,and sides of vehicle 900. In at least one embodiment, vehicle 900 mayuse three surround camera(s) 974 (e.g., left, right, and rear), and mayleverage one or more other camera(s) (e.g., a forward-facing camera) asa fourth surround-view camera.

In at least one embodiment, cameras with a field of view that includeportions of an environment behind vehicle 900 (e.g., rear-view cameras)may be used for parking assistance, surround view, rear collisionwarnings, and creating and updating an occupancy grid. In at least oneembodiment, a wide variety of cameras may be used including, but notlimited to, cameras that are also suitable as a front-facing camera(s)(e.g., long-range cameras 998 and/or mid-range camera(s) 976, stereocamera(s) 968), infrared camera(s) 972, etc.), as described herein.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 9B for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

FIG. 9C is a block diagram illustrating an example system architecturefor autonomous vehicle 900 of FIG. 9A, according to at least oneembodiment. In at least one embodiment, each of components, features,and systems of vehicle 900 in FIG. 9C is illustrated as being connectedvia a bus 902. In at least one embodiment, bus 902 may include, withoutlimitation, a CAN data interface (alternatively referred to herein as a“CAN bus”). In at least one embodiment, a CAN may be a network insidevehicle 900 used to aid in control of various features and functionalityof vehicle 900, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. In at least one embodiment, bus 902may be configured to have dozens or even hundreds of nodes, each withits own unique identifier (e.g., a CAN ID). In at least one embodiment,bus 902 may be read to find steering wheel angle, ground speed, enginerevolutions per minute (“RPMs”), button positions, and/or other vehiclestatus indicators. In at least one embodiment, bus 902 may be a CAN busthat is ASIL B compliant.

In at least one embodiment, in addition to, or alternatively from CAN,FlexRay and/or Ethernet protocols may be used. In at least oneembodiment, there may be any number of busses forming bus 902, which mayinclude, without limitation, zero or more CAN busses, zero or moreFlexRay busses, zero or more Ethernet busses, and/or zero or more othertypes of busses using different protocols. In at least one embodiment,two or more busses may be used to perform different functions, and/ormay be used for redundancy. For example, a first bus may be used forcollision avoidance functionality and a second bus may be used foractuation control. In at least one embodiment, each bus of bus 902 maycommunicate with any of components of vehicle 900, and two or morebusses of bus 902 may communicate with corresponding components. In atleast one embodiment, each of any number of system(s) on chip(s)(“SoC(s)”) 904 (such as SoC 904(A) and SoC 904(B), each of controller(s)936, and/or each computer within vehicle may have access to same inputdata (e.g., inputs from sensors of vehicle 900), and may be connected toa common bus, such CAN bus.

In at least one embodiment, vehicle 900 may include one or morecontroller(s) 936, such as those described herein with respect to FIG.9A. In at least one embodiment, controller(s) 936 may be used for avariety of functions. In at least one embodiment, controller(s) 936 maybe coupled to any of various other components and systems of vehicle900, and may be used for control of vehicle 900, artificial intelligenceof vehicle 900, infotainment for vehicle 900, and/or other functions.

In at least one embodiment, vehicle 900 may include any number of SoCs904. In at least one embodiment, each of SoCs 904 may include, withoutlimitation, central processing units (“CPU(s)”) 906, graphics processingunits (“GPU(s)”) 908, processor(s) 910, cache(s) 912, accelerator(s)914, data store(s) 916, and/or other components and features notillustrated. In at least one embodiment, SoC(s) 904 may be used tocontrol vehicle 900 in a variety of platforms and systems. For example,in at least one embodiment, SoC(s) 904 may be combined in a system(e.g., system of vehicle 900) with a High Definition (“HD”) map 922which may obtain map refreshes and/or updates via network interface 924from one or more servers (not shown in FIG. 9C).

In at least one embodiment, CPU(s) 906 may include a CPU cluster or CPUcomplex (alternatively referred to herein as a “CCPLEX”). In at leastone embodiment, CPU(s) 906 may include multiple cores and/or level two(“L2”) caches. For instance, in at least one embodiment, CPU(s) 906 mayinclude eight cores in a coherent multi-processor configuration. In atleast one embodiment, CPU(s) 906 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 megabyte (MB) L2cache). In at least one embodiment, CPU(s) 906 (e.g., CCPLEX) may beconfigured to support simultaneous cluster operations enabling anycombination of clusters of CPU(s) 906 to be active at any given time.

In at least one embodiment, one or more of CPU(s) 906 may implementpower management capabilities that include, without limitation, one ormore of following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when such core is not actively executing instructionsdue to execution of Wait for Interrupt (“WFI”)/Wait for Event (“WFE”)instructions; each core may be independently power-gated; each corecluster may be independently clock-gated when all cores are clock-gatedor power-gated; and/or each core cluster may be independentlypower-gated when all cores are power-gated. In at least one embodiment,CPU(s) 906 may further implement an enhanced algorithm for managingpower states, where allowed power states and expected wakeup times arespecified, and hardware/microcode determines which best power state toenter for core, cluster, and CCPLEX. In at least one embodiment,processing cores may support simplified power state entry sequences insoftware with work offloaded to microcode.

In at least one embodiment, GPU(s) 908 may include an integrated GPU(alternatively referred to herein as an “iGPU”). In at least oneembodiment, GPU(s) 908 may be programmable and may be efficient forparallel workloads. In at least one embodiment, GPU(s) 908 may use anenhanced tensor instruction set. In at least one embodiment, GPU(s) 908may include one or more streaming microprocessors, where each streamingmicroprocessor may include a level one (“L1”) cache (e.g., an L1 cachewith at least 96 KB storage capacity), and two or more streamingmicroprocessors may share an L2 cache (e.g., an L2 cache with a 512 KBstorage capacity). In at least one embodiment, GPU(s) 908 may include atleast eight streaming microprocessors. In at least one embodiment,GPU(s) 908 may use compute application programming interface(s)(API(s)). In at least one embodiment, GPU(s) 908 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA model).

In at least one embodiment, one or more of GPU(s) 908 may bepower-optimized for best performance in automotive and embedded usecases. For example, in at least one embodiment, GPU(s) 908 could befabricated on Fin field-effect transistor (“FinFET”) circuitry. In atleast one embodiment, each streaming microprocessor may incorporate anumber of mixed-precision processing cores partitioned into multipleblocks. For example, and without limitation, 64 PF32 cores and 32 PF64cores could be partitioned into four processing blocks. In at least oneembodiment, each processing block could be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA Tensor cores fordeep learning matrix arithmetic, a level zero (“L0”) instruction cache,a warp scheduler, a dispatch unit, and/or a 64 KB register file. In atleast one embodiment, streaming microprocessors may include independentparallel integer and floating-point data paths to provide for efficientexecution of workloads with a mix of computation and addressingcalculations. In at least one embodiment, streaming microprocessors mayinclude independent thread scheduling capability to enable finer-grainsynchronization and cooperation between parallel threads. In at leastone embodiment, streaming microprocessors may include a combined L1 datacache and shared memory unit in order to improve performance whilesimplifying programming.

In at least one embodiment, one or more of GPU(s) 908 may include a highbandwidth memory (“HBM) and/or a 16 GB HBM2 memory subsystem to provide,in some examples, about 900 GB/second peak memory bandwidth. In at leastone embodiment, in addition to, or alternatively from, HBM memory, asynchronous graphics random-access memory (“SGRAM”) may be used, such asa graphics double data rate type five synchronous random-access memory(“GDDR5”).

In at least one embodiment, GPU(s) 908 may include unified memorytechnology. In at least one embodiment, address translation services(“ATS”) support may be used to allow GPU(s) 908 to access CPU(s) 906page tables directly. In at least one embodiment, embodiment, when a GPUof GPU(s) 908 memory management unit (“MMU”) experiences a miss, anaddress translation request may be transmitted to CPU(s) 906. Inresponse, 2 CPU of CPU(s) 906 may look in its page tables for avirtual-to-physical mapping for an address and transmit translation backto GPU(s) 908, in at least one embodiment. In at least one embodiment,unified memory technology may allow a single unified virtual addressspace for memory of both CPU(s) 906 and GPU(s) 908, thereby simplifyingGPU(s) 908 programming and porting of applications to GPU(s) 908.

In at least one embodiment, GPU(s) 908 may include any number of accesscounters that may keep track of frequency of access of GPU(s) 908 tomemory of other processors. In at least one embodiment, accesscounter(s) may help ensure that memory pages are moved to physicalmemory of a processor that is accessing pages most frequently, therebyimproving efficiency for memory ranges shared between processors.

In at least one embodiment, one or more of SoC(s) 904 may include anynumber of cache(s) 912, including those described herein. For example,in at least one embodiment, cache(s) 912 could include a level three(“L3”) cache that is available to both CPU(s) 906 and GPU(s) 908 (e.g.,that is connected to CPU(s) 906 and GPU(s) 908). In at least oneembodiment, cache(s) 912 may include a write-back cache that may keeptrack of states of lines, such as by using a cache coherence protocol(e.g., MEI, MESI, MSI, etc.). In at least one embodiment, a L3 cache mayinclude 4 MB of memory or more, depending on embodiment, althoughsmaller cache sizes may be used.

In at least one embodiment, one or more of SoC(s) 904 may include one ormore accelerator(s) 914 (e.g., hardware accelerators, softwareaccelerators, or a combination thereof). In at least one embodiment,SoC(s) 904 may include a hardware acceleration cluster that may includeoptimized hardware accelerators and/or large on-chip memory. In at leastone embodiment, large on-chip memory (e.g., 4 MB of SRAM), may enable ahardware acceleration cluster to accelerate neural networks and othercalculations. In at least one embodiment, a hardware accelerationcluster may be used to complement GPU(s) 908 and to off-load some oftasks of GPU(s) 908 (e.g., to free up more cycles of GPU(s) 908 forperforming other tasks). In at least one embodiment, accelerator(s) 914could be used for targeted workloads (e.g., perception, convolutionalneural networks (“CNNs”), recurrent neural networks (“RNNs”), etc.) thatare stable enough to be amenable to acceleration. In at least oneembodiment, a CNN may include a region-based or regional convolutionalneural networks (“RCNNs”) and Fast RCNNs (e.g., as used for objectdetection) or other type of CNN

In at least one embodiment, accelerator(s) 914 (e.g., hardwareacceleration cluster) may include one or more deep learning accelerator(“DLA”). In at least one embodiment, DLA(s) may include, withoutlimitation, one or more Tensor processing units (“TPUs”) that may beconfigured to provide an additional ten trillion operations per secondfor deep learning applications and inferencing. In at least oneembodiment, TPUs may be accelerators configured to, and optimized for,performing image processing functions (e.g., for CNNs, RCNNs, etc.). Inat least one embodiment, DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. In at least one embodiment, design of DLA(s) may providemore performance per millimeter than a typical general-purpose GPU, andtypically vastly exceeds performance of a CPU. In at least oneembodiment, TPU(s) may perform several functions, including asingle-instance convolution function, supporting, for example, INT8,INT16, and FP16 data types for both features and weights, as well aspost-processor functions. In at least one embodiment, DLA(s) may quicklyand efficiently execute neural networks, especially CNNs, on processedor unprocessed data for any of a variety of functions, including, forexample and without limitation: a CNN for object identification anddetection using data from camera sensors; a CNN for distance estimationusing data from camera sensors; a CNN for emergency vehicle detectionand identification and detection using data from microphones; a CNN forfacial recognition and vehicle owner identification using data fromcamera sensors; and/or a CNN for security and/or safety related events.

In at least one embodiment, DLA(s) may perform any function of GPU(s)908, and by using an inference accelerator, for example, a designer maytarget either DLA(s) or GPU(s) 908 for any function. For example, in atleast one embodiment, a designer may focus processing of CNNs andfloating point operations on DLA(s) and leave other functions to GPU(s)908 and/or accelerator(s) 914.

In at least one embodiment, accelerator(s) 914 may include programmablevision accelerator (“PVA”), which may alternatively be referred toherein as a computer vision accelerator. In at least one embodiment, PVAmay be designed and configured to accelerate computer vision algorithmsfor advanced driver assistance system (“ADAS”) 938, autonomous driving,augmented reality (“AR”) applications, and/or virtual reality (“VR”)applications. In at least one embodiment, PVA may provide a balancebetween performance and flexibility. For example, in at least oneembodiment, each PVA may include, for example and without limitation,any number of reduced instruction set computer (“RISC”) cores, directmemory access (“DMA”), and/or any number of vector processors.

In at least one embodiment, RISC cores may interact with image sensors(e.g., image sensors of any cameras described herein), image signalprocessor(s), etc. In at least one embodiment, each RISC core mayinclude any amount of memory. In at least one embodiment, RISC cores mayuse any of a number of protocols, depending on embodiment. In at leastone embodiment, RISC cores may execute a real-time operating system(“RTOS”). In at least one embodiment, RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (“ASICs”), and/or memory devices. For example, in atleast one embodiment, RISC cores could include an instruction cacheand/or a tightly coupled RAM.

In at least one embodiment, DMA may enable components of PVA to accesssystem memory independently of CPU(s) 906. In at least one embodiment,DMA may support any number of features used to provide optimization to aPVA including, but not limited to, supporting multi-dimensionaladdressing and/or circular addressing. In at least one embodiment, DMAmay support up to six or more dimensions of addressing, which mayinclude, without limitation, block width, block height, block depth,horizontal block stepping, vertical block stepping, and/or depthstepping.

In at least one embodiment, vector processors may be programmableprocessors that may be designed to efficiently and flexibly executeprogramming for computer vision algorithms and provide signal processingcapabilities. In at least one embodiment, a PVA may include a PVA coreand two vector processing subsystem partitions. In at least oneembodiment, a PVA core may include a processor subsystem, DMA engine(s)(e.g., two DMA engines), and/or other peripherals. In at least oneembodiment, a vector processing subsystem may operate as a primaryprocessing engine of a PVA, and may include a vector processing unit(“VPU”), an instruction cache, and/or vector memory (e.g., “VMEM”). Inat least one embodiment, VPU core may include a digital signal processorsuch as, for example, a single instruction, multiple data (“SIMD”), verylong instruction word (“VLIW”) digital signal processor. In at least oneembodiment, a combination of SIMD and VLIW may enhance throughput andspeed.

In at least one embodiment, each of vector processors may include aninstruction cache and may be coupled to dedicated memory. As a result,in at least one embodiment, each of vector processors may be configuredto execute independently of other vector processors. In at least oneembodiment, vector processors that are included in a particular PVA maybe configured to employ data parallelism. For instance, in at least oneembodiment, plurality of vector processors included in a single PVA mayexecute a common computer vision algorithm, but on different regions ofan image. In at least one embodiment, vector processors included in aparticular PVA may simultaneously execute different computer visionalgorithms, on one image, or even execute different algorithms onsequential images or portions of an image. In at least one embodiment,among other things, any number of PVAs may be included in hardwareacceleration cluster and any number of vector processors may be includedin each PVA. In at least one embodiment, PVA may include additionalerror correcting code (“ECC”) memory, to enhance overall system safety.

In at least one embodiment, accelerator(s) 914 may include a computervision network on-chip and static random-access memory (“SRAM”), forproviding a high-bandwidth, low latency SRAM for accelerator(s) 914. Inat least one embodiment, on-chip memory may include at least 4 MB SRAM,comprising, for example and without limitation, eight field-configurablememory blocks, that may be accessible by both a PVA and a DLA. In atleast one embodiment, each pair of memory blocks may include an advancedperipheral bus (“APB”) interface, configuration circuitry, a controller,and a multiplexer. In at least one embodiment, any type of memory may beused. In at least one embodiment, a PVA and a DLA may access memory viaa backbone that provides a PVA and a DLA with high-speed access tomemory. In at least one embodiment, a backbone may include a computervision network on-chip that interconnects a PVA and a DLA to memory(e.g., using APB).

In at least one embodiment, a computer vision network on-chip mayinclude an interface that determines, before transmission of any controlsignal/address/data, that both a PVA and a DLA provide ready and validsignals. In at least one embodiment, an interface may provide forseparate phases and separate channels for transmitting controlsignals/addresses/data, as well as burst-type communications forcontinuous data transfer. In at least one embodiment, an interface maycomply with International Organization for Standardization (“ISO”) 26262or International Electrotechnical Commission (“IEC”) 61508 standards,although other standards and protocols may be used.

In at least one embodiment, one or more of SoC(s) 904 may include areal-time ray-tracing hardware accelerator. In at least one embodiment,real-time ray-tracing hardware accelerator may be used to quickly andefficiently determine positions and extents of objects (e.g., within aworld model), to generate real-time visualization simulations, for RADARsignal interpretation, for sound propagation synthesis and/or analysis,for simulation of SONAR systems, for general wave propagationsimulation, for comparison to LIDAR data for purposes of localizationand/or other functions, and/or for other uses.

In at least one embodiment, accelerator(s) 914 can have a wide array ofuses for autonomous driving. In at least one embodiment, a PVA may beused for key processing stages in ADAS and autonomous vehicles. In atleast one embodiment, a PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, a PVA performs well on semi-dense or denseregular computation, even on small data sets, which might requirepredictable run-times with low latency and low power. In at least oneembodiment, such as in vehicle 900, PVAs might be designed to runclassic computer vision algorithms, as they can be efficient at objectdetection and operating on integer math.

For example, according to at least one embodiment of technology, a PVAis used to perform computer stereo vision. In at least one embodiment, asemi-global matching-based algorithm may be used in some examples,although this is not intended to be limiting. In at least oneembodiment, applications for Level 3-5 autonomous driving use motionestimation/stereo matching on-the-fly (e.g., structure from motion,pedestrian recognition, lane detection, etc.). In at least oneembodiment, a PVA may perform computer stereo vision functions on inputsfrom two monocular cameras.

In at least one embodiment, a PVA may be used to perform dense opticalflow. For example, in at least one embodiment, a PVA could process rawRADAR data (e.g., using a 4D Fast Fourier Transform) to provideprocessed RADAR data. In at least one embodiment, a PVA is used for timeof flight depth processing, by processing raw time of flight data toprovide processed time of flight data, for example.

In at least one embodiment, a DLA may be used to run any type of networkto enhance control and driving safety, including for example and withoutlimitation, a neural network that outputs a measure of confidence foreach object detection. In at least one embodiment, confidence may berepresented or interpreted as a probability, or as providing a relative“weight” of each detection compared to other detections. In at least oneembodiment, a confidence measure enables a system to make furtherdecisions regarding which detections should be considered as truepositive detections rather than false positive detections. In at leastone embodiment, a system may set a threshold value for confidence andconsider only detections exceeding threshold value as true positivedetections. In an embodiment in which an automatic emergency braking(“AEB”) system is used, false positive detections would cause vehicle toautomatically perform emergency braking, which is obviously undesirable.In at least one embodiment, highly confident detections may beconsidered as triggers for AEB. In at least one embodiment, a DLA mayrun a neural network for regressing confidence value. In at least oneembodiment, neural network may take as its input at least some subset ofparameters, such as bounding box dimensions, ground plane estimateobtained (e.g., from another subsystem), output from IMU sensor(s) 966that correlates with vehicle 900 orientation, distance, 3D locationestimates of object obtained from neural network and/or other sensors(e.g., LIDAR sensor(s) 964 or RADAR sensor(s) 960), among others.

In at least one embodiment, one or more of SoC(s) 904 may include datastore(s) 916 (e.g., memory). In at least one embodiment, data store(s)916 may be on-chip memory of SoC(s) 904, which may store neural networksto be executed on GPU(s) 908 and/or a DLA. In at least one embodiment,data store(s) 916 may be large enough in capacity to store multipleinstances of neural networks for redundancy and safety. In at least oneembodiment, data store(s) 916 may comprise L2 or L3 cache(s).

In at least one embodiment, one or more of SoC(s) 904 may include anynumber of processor(s) 910 (e.g., embedded processors). In at least oneembodiment, processor(s) 910 may include a boot and power managementprocessor that may be a dedicated processor and subsystem to handle bootpower and management functions and related security enforcement. In atleast one embodiment, a boot and power management processor may be apart of a boot sequence of SoC(s) 904 and may provide runtime powermanagement services. In at least one embodiment, a boot power andmanagement processor may provide clock and voltage programming,assistance in system low power state transitions, management of SoC(s)904 thermals and temperature sensors, and/or management of SoC(s) 904power states. In at least one embodiment, each temperature sensor may beimplemented as a ring-oscillator whose output frequency is proportionalto temperature, and SoC(s) 904 may use ring-oscillators to detecttemperatures of CPU(s) 906, GPU(s) 908, and/or accelerator(s) 914. In atleast one embodiment, if temperatures are determined to exceed athreshold, then a boot and power management processor may enter atemperature fault routine and put SoC(s) 904 into a lower power stateand/or put vehicle 900 into a chauffeur to safe stop mode (e.g., bringvehicle 900 to a safe stop).

In at least one embodiment, processor(s) 910 may further include a setof embedded processors that may serve as an audio processing enginewhich may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In at least one embodiment, an audioprocessing engine is a dedicated processor core with a digital signalprocessor with dedicated RAM.

In at least one embodiment, processor(s) 910 may further include analways-on processor engine that may provide necessary hardware featuresto support low power sensor management and wake use cases. In at leastone embodiment, an always-on processor engine may include, withoutlimitation, a processor core, a tightly coupled RAM, supportingperipherals (e.g., timers and interrupt controllers), various I/Ocontroller peripherals, and routing logic.

In at least one embodiment, processor(s) 910 may further include asafety cluster engine that includes, without limitation, a dedicatedprocessor subsystem to handle safety management for automotiveapplications. In at least one embodiment, a safety cluster engine mayinclude, without limitation, two or more processor cores, a tightlycoupled RAM, support peripherals (e.g., timers, an interrupt controller,etc.), and/or routing logic. In a safety mode, two or more cores mayoperate, in at least one embodiment, in a lockstep mode and function asa single core with comparison logic to detect any differences betweentheir operations. In at least one embodiment, processor(s) 910 mayfurther include a real-time camera engine that may include, withoutlimitation, a dedicated processor subsystem for handling real-timecamera management. In at least one embodiment, processor(s) 910 mayfurther include a high-dynamic range signal processor that may include,without limitation, an image signal processor that is a hardware enginethat is part of a camera processing pipeline.

In at least one embodiment, processor(s) 910 may include a video imagecompositor that may be a processing block (e.g., implemented on amicroprocessor) that implements video post-processing functions neededby a video playback application to produce a final image for a playerwindow. In at least one embodiment, a video image compositor may performlens distortion correction on wide-view camera(s) 970, surroundcamera(s) 974, and/or on in-cabin monitoring camera sensor(s). In atleast one embodiment, in-cabin monitoring camera sensor(s) arepreferably monitored by a neural network running on another instance ofSoC 904, configured to identify in cabin events and respond accordingly.In at least one embodiment, an in-cabin system may perform, withoutlimitation, lip reading to activate cellular service and place a phonecall, dictate emails, change a vehicle's destination, activate or changea vehicle's infotainment system and settings, or provide voice-activatedweb surfing. In at least one embodiment, certain functions are availableto a driver when a vehicle is operating in an autonomous mode and aredisabled otherwise.

In at least one embodiment, a video image compositor may includeenhanced temporal noise reduction for both spatial and temporal noisereduction. For example, in at least one embodiment, where motion occursin a video, noise reduction weights spatial information appropriately,decreasing weights of information provided by adjacent frames. In atleast one embodiment, where an image or portion of an image does notinclude motion, temporal noise reduction performed by video imagecompositor may use information from a previous image to reduce noise ina current image.

In at least one embodiment, a video image compositor may also beconfigured to perform stereo rectification on input stereo lens frames.In at least one embodiment, a video image compositor may further be usedfor user interface composition when an operating system desktop is inuse, and GPU(s) 908 are not required to continuously render newsurfaces. In at least one embodiment, when GPU(s) 908 are powered on andactive doing 3D rendering, a video image compositor may be used tooffload GPU(s) 908 to improve performance and responsiveness.

In at least one embodiment, one or more SoC of SoC(s) 904 may furtherinclude a mobile industry processor interface (“MIPI”) camera serialinterface for receiving video and input from cameras, a high-speedinterface, and/or a video input block that may be used for a camera andrelated pixel input functions. In at least one embodiment, one or moreof SoC(s) 904 may further include an input/output controller(s) that maybe controlled by software and may be used for receiving I/O signals thatare uncommitted to a specific role.

In at least one embodiment, one or more Soc of SoC(s) 904 may furtherinclude a broad range of peripheral interfaces to enable communicationwith peripherals, audio encoders/decoders (“codecs”), power management,and/or other devices. In at least one embodiment, SoC(s) 904 may be usedto process data from cameras (e.g., connected over Gigabit MultimediaSerial Link and Ethernet channels), sensors (e.g., LIDAR sensor(s) 964,RADAR sensor(s) 960, etc. that may be connected over Ethernet channels),data from bus 902 (e.g., speed of vehicle 900, steering wheel position,etc.), data from GNSS sensor(s) 958 (e.g., connected over a Ethernet busor a CAN bus), etc. In at least one embodiment, one or more SoC ofSoC(s) 904 may further include dedicated high-performance mass storagecontrollers that may include their own DMA engines, and that may be usedto free CPU(s) 906 from routine data management tasks.

In at least one embodiment, SoC(s) 904 may be an end-to-end platformwith a flexible architecture that spans automation Levels 3-5, therebyproviding a comprehensive functional safety architecture that leveragesand makes efficient use of computer vision and ADAS techniques fordiversity and redundancy, and provides a platform for a flexible,reliable driving software stack, along with deep learning tools. In atleast one embodiment, SoC(s) 904 may be faster, more reliable, and evenmore energy-efficient and space-efficient than conventional systems. Forexample, in at least one embodiment, accelerator(s) 914, when combinedwith CPU(s) 906, GPU(s) 908, and data store(s) 916, may provide for afast, efficient platform for Level 3-5 autonomous vehicles.

In at least one embodiment, computer vision algorithms may be executedon CPUs, which may be configured using a high-level programminglanguage, such as C, to execute a wide variety of processing algorithmsacross a wide variety of visual data. However, in at least oneembodiment, CPUs are oftentimes unable to meet performance requirementsof many computer vision applications, such as those related to executiontime and power consumption, for example. In at least one embodiment,many CPUs are unable to execute complex object detection algorithms inreal-time, which is used in in-vehicle ADAS applications and inpractical Level 3-5 autonomous vehicles.

Embodiments described herein allow for multiple neural networks to beperformed simultaneously and/or sequentially, and for results to becombined together to enable Level 3-5 autonomous driving functionality.For example, in at least one embodiment, a CNN executing on a DLA or adiscrete GPU (e.g., GPU(s) 920) may include text and word recognition,allowing reading and understanding of traffic signs, including signs forwhich a neural network has not been specifically trained. In at leastone embodiment, a DLA may further include a neural network that is ableto identify, interpret, and provide semantic understanding of a sign,and to pass that semantic understanding to path planning modules runningon a CPU Complex.

In at least one embodiment, multiple neural networks may be runsimultaneously, as for Level 3, 4, or 5 driving. For example, in atleast one embodiment, a warning sign stating “Caution: flashing lightsindicate icy conditions,” along with an electric light, may beindependently or collectively interpreted by several neural networks. Inat least one embodiment, such warning sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), text “flashing lights indicate icy conditions”may be interpreted by a second deployed neural network, which informs avehicle's path planning software (preferably executing on a CPU Complex)that when flashing lights are detected, icy conditions exist. In atleast one embodiment, a flashing light may be identified by operating athird deployed neural network over multiple frames, informing avehicle's path-planning software of a presence (or an absence) offlashing lights. In at least one embodiment, all three neural networksmay run simultaneously, such as within a DLA and/or on GPU(s) 908.

In at least one embodiment, a CNN for facial recognition and vehicleowner identification may use data from camera sensors to identifypresence of an authorized driver and/or owner of vehicle 900. In atleast one embodiment, an always-on sensor processing engine may be usedto unlock a vehicle when an owner approaches a driver door and turns onlights, and, in a security mode, to disable such vehicle when an ownerleaves such vehicle. In this way, SoC(s) 904 provide for securityagainst theft and/or carjacking.

In at least one embodiment, a CNN for emergency vehicle detection andidentification may use data from microphones 996 to detect and identifyemergency vehicle sirens. In at least one embodiment, SoC(s) 904 use aCNN for classifying environmental and urban sounds, as well asclassifying visual data. In at least one embodiment, a CNN running on aDLA is trained to identify a relative closing speed of an emergencyvehicle (e.g., by using a Doppler effect). In at least one embodiment, aCNN may also be trained to identify emergency vehicles specific to alocal area in which a vehicle is operating, as identified by GNSSsensor(s) 958. In at least one embodiment, when operating in Europe, aCNN will seek to detect European sirens, and when in North America, aCNN will seek to identify only North American sirens. In at least oneembodiment, once an emergency vehicle is detected, a control program maybe used to execute an emergency vehicle safety routine, slowing avehicle, pulling over to a side of a road, parking a vehicle, and/oridling a vehicle, with assistance of ultrasonic sensor(s) 962, untilemergency vehicles pass.

In at least one embodiment, vehicle 900 may include CPU(s) 918 (e.g.,discrete CPU(s), or dCPU(s)), that may be coupled to SoC(s) 904 via ahigh-speed interconnect (e.g., PCIe). In at least one embodiment, CPU(s)918 may include an X86 processor, for example. CPU(s) 918 may be used toperform any of a variety of functions, including arbitrating potentiallyinconsistent results between ADAS sensors and SoC(s) 904, and/ormonitoring status and health of controller(s) 936 and/or an infotainmentsystem on a chip (“infotainment SoC”) 930, for example.

In at least one embodiment, vehicle 900 may include GPU(s) 920 (e.g.,discrete GPU(s), or dGPU(s)), that may be coupled to SoC(s) 904 via ahigh-speed interconnect (e.g., NVIDIA's NVLINK channel). In at least oneembodiment, GPU(s) 920 may provide additional artificial intelligencefunctionality, such as by executing redundant and/or different neuralnetworks, and may be used to train and/or update neural networks basedat least in part on input (e.g., sensor data) from sensors of a vehicle900.

In at least one embodiment, vehicle 900 may further include networkinterface 924 which may include, without limitation, wireless antenna(s)926 (e.g., one or more wireless antennas for different communicationprotocols, such as a cellular antenna, a Bluetooth antenna, etc.). In atleast one embodiment, network interface 924 may be used to enablewireless connectivity to Internet cloud services (e.g., with server(s)and/or other network devices), with other vehicles, and/or withcomputing devices (e.g., client devices of passengers). In at least oneembodiment, to communicate with other vehicles, a direct link may beestablished between vehicle 90 and another vehicle and/or an indirectlink may be established (e.g., across networks and over the Internet).In at least one embodiment, direct links may be provided using avehicle-to-vehicle communication link. In at least one embodiment, avehicle-to-vehicle communication link may provide vehicle 900information about vehicles in proximity to vehicle 900 (e.g., vehiclesin front of, on a side of, and/or behind vehicle 900). In at least oneembodiment, such aforementioned functionality may be part of acooperative adaptive cruise control functionality of vehicle 900.

In at least one embodiment, network interface 924 may include an SoCthat provides modulation and demodulation functionality and enablescontroller(s) 936 to communicate over wireless networks. In at least oneembodiment, network interface 924 may include a radio frequencyfront-end for up-conversion from baseband to radio frequency, and downconversion from radio frequency to baseband. In at least one embodiment,frequency conversions may be performed in any technically feasiblefashion. For example, frequency conversions could be performed throughwell-known processes, and/or using super-heterodyne processes. In atleast one embodiment, radio frequency front end functionality may beprovided by a separate chip. In at least one embodiment, networkinterfaces may include wireless functionality for communicating overLTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave,ZigBee, LoRaWAN, and/or other wireless protocols.

In at least one embodiment, vehicle 900 may further include datastore(s) 928 which may include, without limitation, off-chip (e.g., offSoC(s) 904) storage. In at least one embodiment, data store(s) 928 mayinclude, without limitation, one or more storage elements including RAM,SRAM, dynamic random-access memory (“DRAM”), video random-access memory(“VRAM”), flash memory, hard disks, and/or other components and/ordevices that may store at least one bit of data.

In at least one embodiment, vehicle 900 may further include GNSSsensor(s) 958 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. In at least one embodiment, any number of GNSS sensor(s) 958may be used, including, for example and without limitation, a GPS usinga USB connector with an Ethernet-to-Serial (e.g., RS-232) bridge.

In at least one embodiment, vehicle 900 may further include RADARsensor(s) 960. In at least one embodiment, RADAR sensor(s) 960 may beused by vehicle 900 for long-range vehicle detection, even in darknessand/or severe weather conditions. In at least one embodiment, RADARfunctional safety levels may be ASIL B. In at least one embodiment,RADAR sensor(s) 960 may use a CAN bus and/or bus 902 (e.g., to transmitdata generated by RADAR sensor(s) 960) for control and to access objecttracking data, with access to Ethernet channels to access raw data insome examples. In at least one embodiment, a wide variety of RADARsensor types may be used. For example, and without limitation, RADARsensor(s) 960 may be suitable for front, rear, and side RADAR use. In atleast one embodiment, one or more sensor of RADAR sensors(s) 960 is aPulse Doppler RADAR sensor.

In at least one embodiment, RADAR sensor(s) 960 may include differentconfigurations, such as long-range with narrow field of view,short-range with wide field of view, short-range side coverage, etc. Inat least one embodiment, long-range RADAR may be used for adaptivecruise control functionality. In at least one embodiment, long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m (meter) range. In at least oneembodiment, RADAR sensor(s) 960 may help in distinguishing betweenstatic and moving objects, and may be used by ADAS system 938 foremergency brake assist and forward collision warning. In at least oneembodiment, sensors 960(s) included in a long-range RADAR system mayinclude, without limitation, monostatic multimodal RADAR with multiple(e.g., six or more) fixed RADAR antennae and a high-speed CAN andFlexRay interface. In at least one embodiment, with six antennae, acentral four antennae may create a focused beam pattern, designed torecord vehicle's 900 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. In at least one embodiment,another two antennae may expand field of view, making it possible toquickly detect vehicles entering or leaving a lane of vehicle 900.

In at least one embodiment, mid-range RADAR systems may include, as anexample, a range of up to 160 m (front) or 80 m (rear), and a field ofview of up to 42 degrees (front) or 150 degrees (rear). In at least oneembodiment, short-range RADAR systems may include, without limitation,any number of RADAR sensor(s) 960 designed to be installed at both endsof a rear bumper. When installed at both ends of a rear bumper, in atleast one embodiment, a RADAR sensor system may create two beams thatconstantly monitor blind spots in a rear direction and next to avehicle. In at least one embodiment, short-range RADAR systems may beused in ADAS system 938 for blind spot detection and/or lane changeassist.

In at least one embodiment, vehicle 900 may further include ultrasonicsensor(s) 962. In at least one embodiment, ultrasonic sensor(s) 962,which may be positioned at a front, a back, and/or side location ofvehicle 900, may be used for parking assist and/or to create and updatean occupancy grid. In at least one embodiment, a wide variety ofultrasonic sensor(s) 962 may be used, and different ultrasonic sensor(s)962 may be used for different ranges of detection (e.g., 2.5 m, 4 m). Inat least one embodiment, ultrasonic sensor(s) 962 may operate atfunctional safety levels of ASIL B.

In at least one embodiment, vehicle 900 may include LIDAR sensor(s) 964.In at least one embodiment, LIDAR sensor(s) 964 may be used for objectand pedestrian detection, emergency braking, collision avoidance, and/orother functions. In at least one embodiment, LIDAR sensor(s) 964 mayoperate at functional safety level ASIL B. In at least one embodiment,vehicle 900 may include multiple LIDAR sensors 964 (e.g., two, four,six, etc.) that may use an Ethernet channel (e.g., to provide data to aGigabit Ethernet switch).

In at least one embodiment, LIDAR sensor(s) 964 may be capable ofproviding a list of objects and their distances for a 360-degree fieldof view. In at least one embodiment, commercially available LIDARsensor(s) 964 may have an advertised range of approximately 100 m, withan accuracy of 2 cm to 3 cm, and with support for a 100 Mbps Ethernetconnection, for example. In at least one embodiment, one or morenon-protruding LIDAR sensors may be used. In such an embodiment, LIDARsensor(s) 964 may include a small device that may be embedded into afront, a rear, a side, and/or a corner location of vehicle 900. In atleast one embodiment, LIDAR sensor(s) 964, in such an embodiment, mayprovide up to a 120-degree horizontal and 35-degree verticalfield-of-view, with a 200 m range even for low-reflectivity objects. Inat least one embodiment, front-mounted LIDAR sensor(s) 964 may beconfigured for a horizontal field of view between 45 degrees and 135degrees.

In at least one embodiment, LIDAR technologies, such as 3D flash LIDAR,may also be used. In at least one embodiment, 3D flash LIDAR uses aflash of a laser as a transmission source, to illuminate surroundings ofvehicle 900 up to approximately 200 m. In at least one embodiment, aflash LIDAR unit includes, without limitation, a receptor, which recordslaser pulse transit time and reflected light on each pixel, which inturn corresponds to a range from vehicle 900 to objects. In at least oneembodiment, flash LIDAR may allow for highly accurate anddistortion-free images of surroundings to be generated with every laserflash. In at least one embodiment, four flash LIDAR sensors may bedeployed, one at each side of vehicle 900. In at least one embodiment,3D flash LIDAR systems include, without limitation, a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). In at least one embodiment, flash LIDARdevice may use a 5 nanosecond class I (eye-safe) laser pulse per frameand may capture reflected laser light as a 3D range point cloud andco-registered intensity data.

In at least one embodiment, vehicle 900 may further include IMUsensor(s) 966. In at least one embodiment, IMU sensor(s) 966 may belocated at a center of a rear axle of vehicle 900. In at least oneembodiment, IMU sensor(s) 966 may include, for example and withoutlimitation, accelerometer(s), magnetometer(s), gyroscope(s), a magneticcompass, magnetic compasses, and/or other sensor types. In at least oneembodiment, such as in six-axis applications, IMU sensor(s) 966 mayinclude, without limitation, accelerometers and gyroscopes. In at leastone embodiment, such as in nine-axis applications, IMU sensor(s) 966 mayinclude, without limitation, accelerometers, gyroscopes, andmagnetometers.

In at least one embodiment, IMU sensor(s) 966 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(“GPS/INS”) that combines micro-electro-mechanical systems (“MEMS”)inertial sensors, a high-sensitivity GPS receiver, and advanced Kalmanfiltering algorithms to provide estimates of position, velocity, andattitude. In at least one embodiment, IMU sensor(s) 966 may enablevehicle 900 to estimate its heading without requiring input from amagnetic sensor by directly observing and correlating changes invelocity from a GPS to IMU sensor(s) 966. In at least one embodiment,IMU sensor(s) 966 and GNSS sensor(s) 958 may be combined in a singleintegrated unit.

In at least one embodiment, vehicle 900 may include microphone(s) 996placed in and/or around vehicle 900. In at least one embodiment,microphone(s) 996 may be used for emergency vehicle detection andidentification, among other things.

In at least one embodiment, vehicle 900 may further include any numberof camera types, including stereo camera(s) 968, wide-view camera(s)970, infrared camera(s) 972, surround camera(s) 974, long-rangecamera(s) 998, mid-range camera(s) 976, and/or other camera types. In atleast one embodiment, cameras may be used to capture image data aroundan entire periphery of vehicle 900. In at least one embodiment, whichtypes of cameras used depends on vehicle 900. In at least oneembodiment, any combination of camera types may be used to providenecessary coverage around vehicle 900. In at least one embodiment, anumber of cameras deployed may differ depending on embodiment. Forexample, in at least one embodiment, vehicle 900 could include sixcameras, seven cameras, ten cameras, twelve cameras, or another numberof cameras. In at least one embodiment, cameras may support, as anexample and without limitation, Gigabit Multimedia Serial Link (“GMSL”)and/or Gigabit Ethernet communications. In at least one embodiment, eachcamera might be as described with more detail previously herein withrespect to FIG. 9A and FIG. 9B.

In at least one embodiment, vehicle 900 may further include vibrationsensor(s) 942. In at least one embodiment, vibration sensor(s) 942 maymeasure vibrations of components of vehicle 900, such as axle(s). Forexample, in at least one embodiment, changes in vibrations may indicatea change in road surfaces. In at least one embodiment, when two or morevibration sensors 942 are used, differences between vibrations may beused to determine friction or slippage of road surface (e.g., when adifference in vibration is between a power-driven axle and a freelyrotating axle).

In at least one embodiment, vehicle 900 may include ADAS system 938. Inat least one embodiment, ADAS system 938 may include, withoutlimitation, an SoC, in some examples. In at least one embodiment, ADASsystem 938 may include, without limitation, any number and combinationof an autonomous/adaptive/automatic cruise control (“ACC”) system, acooperative adaptive cruise control (“CACC”) system, a forward crashwarning (“FCW”) system, an automatic emergency braking (“AEB”) system, alane departure warning (“LDW)” system, a lane keep assist (“LKA”)system, a blind spot warning (“BSW”) system, a rear cross-trafficwarning (“RCTW”) system, a collision warning (“CW”) system, a lanecentering (“LC”) system, and/or other systems, features, and/orfunctionality.

In at least one embodiment, ACC system may use RADAR sensor(s) 960,LIDAR sensor(s) 964, and/or any number of camera(s). In at least oneembodiment, ACC system may include a longitudinal ACC system and/or alateral ACC system. In at least one embodiment, a longitudinal ACCsystem monitors and controls distance to another vehicle immediatelyahead of vehicle 900 and automatically adjusts speed of vehicle 900 tomaintain a safe distance from vehicles ahead. In at least oneembodiment, a lateral ACC system performs distance keeping, and advisesvehicle 900 to change lanes when necessary. In at least one embodiment,a lateral ACC is related to other ADAS applications, such as LC and CW.

In at least one embodiment, a CACC system uses information from othervehicles that may be received via network interface 924 and/or wirelessantenna(s) 926 from other vehicles via a wireless link, or indirectly,over a network connection (e.g., over the Internet). In at least oneembodiment, direct links may be provided by a vehicle-to-vehicle (“V2V”)communication link, while indirect links may be provided by aninfrastructure-to-vehicle (“I2V”) communication link. In general, V2Vcommunication provides information about immediately preceding vehicles(e.g., vehicles immediately ahead of and in same lane as vehicle 900),while I2V communication provides information about traffic furtherahead. In at least one embodiment, a CACC system may include either orboth I2V and V2V information sources. In at least one embodiment, giveninformation of vehicles ahead of vehicle 900, a CACC system may be morereliable and it has potential to improve traffic flow smoothness andreduce congestion on road.

In at least one embodiment, an FCW system is designed to alert a driverto a hazard, so that such driver may take corrective action. In at leastone embodiment, an FCW system uses a front-facing camera and/or RADARsensor(s) 960, coupled to a dedicated processor, DSP, FPGA, and/or ASIC,that is electrically coupled to provide driver feedback, such as adisplay, speaker, and/or vibrating component. In at least oneembodiment, an FCW system may provide a warning, such as in form of asound, visual warning, vibration and/or a quick brake pulse.

In at least one embodiment, an AEB system detects an impending forwardcollision with another vehicle or other object, and may automaticallyapply brakes if a driver does not take corrective action within aspecified time or distance parameter. In at least one embodiment, AEBsystem may use front-facing camera(s) and/or RADAR sensor(s) 960,coupled to a dedicated processor, DSP, FPGA, and/or ASIC. In at leastone embodiment, when an AEB system detects a hazard, it will typicallyfirst alert a driver to take corrective action to avoid collision and,if that driver does not take corrective action, that AEB system mayautomatically apply brakes in an effort to prevent, or at leastmitigate, an impact of a predicted collision. In at least oneembodiment, an AEB system may include techniques such as dynamic brakesupport and/or crash imminent braking.

In at least one embodiment, an LDW system provides visual, audible,and/or tactile warnings, such as steering wheel or seat vibrations, toalert driver when vehicle 900 crosses lane markings. In at least oneembodiment, an LDW system does not activate when a driver indicates anintentional lane departure, such as by activating a turn signal. In atleast one embodiment, an LDW system may use front-side facing cameras,coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to provide driver feedback, such as a display,speaker, and/or vibrating component. In at least one embodiment, an LKAsystem is a variation of an LDW system. In at least one embodiment, anLKA system provides steering input or braking to correct vehicle 900 ifvehicle 900 starts to exit its lane.

In at least one embodiment, a BSW system detects and warns a driver ofvehicles in an automobile's blind spot. In at least one embodiment, aBSW system may provide a visual, audible, and/or tactile alert toindicate that merging or changing lanes is unsafe. In at least oneembodiment, a BSW system may provide an additional warning when a driveruses a turn signal. In at least one embodiment, a BSW system may userear-side facing camera(s) and/or RADAR sensor(s) 960, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

In at least one embodiment, an RCTW system may provide visual, audible,and/or tactile notification when an object is detected outside arear-camera range when vehicle 900 is backing up. In at least oneembodiment, an RCTW system includes an AEB system to ensure that vehiclebrakes are applied to avoid a crash. In at least one embodiment, an RCTWsystem may use one or more rear-facing RADAR sensor(s) 960, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to provide driver feedback, such as a display, speaker, and/orvibrating component.

In at least one embodiment, conventional ADAS systems may be prone tofalse positive results which may be annoying and distracting to adriver, but typically are not catastrophic, because conventional ADASsystems alert a driver and allow that driver to decide whether a safetycondition truly exists and act accordingly. In at least one embodiment,vehicle 900 itself decides, in case of conflicting results, whether toheed result from a primary computer or a secondary computer (e.g., afirst controller or a second controller of controllers 936). Forexample, in at least one embodiment, ADAS system 938 may be a backupand/or secondary computer for providing perception information to abackup computer rationality module. In at least one embodiment, a backupcomputer rationality monitor may run redundant diverse software onhardware components to detect faults in perception and dynamic drivingtasks. In at least one embodiment, outputs from ADAS system 938 may beprovided to a supervisory MCU. In at least one embodiment, if outputsfrom a primary computer and outputs from a secondary computer conflict,a supervisory MCU determines how to reconcile conflict to ensure safeoperation.

In at least one embodiment, a primary computer may be configured toprovide a supervisory MCU with a confidence score, indicating thatprimary computer's confidence in a chosen result. In at least oneembodiment, if that confidence score exceeds a threshold, thatsupervisory MCU may follow that primary computer's direction, regardlessof whether that secondary computer provides a conflicting orinconsistent result. In at least one embodiment, where a confidencescore does not meet a threshold, and where primary and secondarycomputers indicate different results (e.g., a conflict), a supervisoryMCU may arbitrate between computers to determine an appropriate outcome.

In at least one embodiment, a supervisory MCU may be configured to run aneural network(s) that is trained and configured to determine, based atleast in part on outputs from a primary computer and outputs from asecondary computer, conditions under which that secondary computerprovides false alarms. In at least one embodiment, neural network(s) ina supervisory MCU may learn when a secondary computer's output may betrusted, and when it cannot. For example, in at least one embodiment,when that secondary computer is a RADAR-based FCW system, a neuralnetwork(s) in that supervisory MCU may learn when an FCW system isidentifying metallic objects that are not, in fact, hazards, such as adrainage grate or manhole cover that triggers an alarm. In at least oneembodiment, when a secondary computer is a camera-based LDW system, aneural network in a supervisory MCU may learn to override LDW whenbicyclists or pedestrians are present and a lane departure is, in fact,a safest maneuver. In at least one embodiment, a supervisory MCU mayinclude at least one of a DLA or a GPU suitable for running neuralnetwork(s) with associated memory. In at least one embodiment, asupervisory MCU may comprise and/or be included as a component of SoC(s)904.

In at least one embodiment, ADAS system 938 may include a secondarycomputer that performs ADAS functionality using traditional rules ofcomputer vision. In at least one embodiment, that secondary computer mayuse classic computer vision rules (if-then), and presence of a neuralnetwork(s) in a supervisory MCU may improve reliability, safety andperformance. For example, in at least one embodiment, diverseimplementation and intentional non-identity makes an overall system morefault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, in at least oneembodiment, if there is a software bug or error in software running on aprimary computer, and non-identical software code running on a secondarycomputer provides a consistent overall result, then a supervisory MCUmay have greater confidence that an overall result is correct, and a bugin software or hardware on that primary computer is not causing amaterial error.

In at least one embodiment, an output of ADAS system 938 may be fed intoa primary computer's perception block and/or a primary computer'sdynamic driving task block. For example, in at least one embodiment, ifADAS system 938 indicates a forward crash warning due to an objectimmediately ahead, a perception block may use this information whenidentifying objects. In at least one embodiment, a secondary computermay have its own neural network that is trained and thus reduces a riskof false positives, as described herein.

In at least one embodiment, vehicle 900 may further include infotainmentSoC 930 (e.g., an in-vehicle infotainment system (IVI)). Althoughillustrated and described as an SoC, infotainment system SoC 930, in atleast one embodiment, may not be an SoC, and may include, withoutlimitation, two or more discrete components. In at least one embodiment,infotainment SoC 930 may include, without limitation, a combination ofhardware and software that may be used to provide audio (e.g., music, apersonal digital assistant, navigational instructions, news, radio,etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g.,hands-free calling), network connectivity (e.g., LTE, WiFi, etc.),and/or information services (e.g., navigation systems, rear-parkingassistance, a radio data system, vehicle related information such asfuel level, total distance covered, brake fuel level, oil level, dooropen/close, air filter information, etc.) to vehicle 900. For example,infotainment SoC 930 could include radios, disk players, navigationsystems, video players, USB and Bluetooth connectivity, carputers,in-car entertainment, WiFi, steering wheel audio controls, hands freevoice control, a heads-up display (“HUD”), HMI display 934, a telematicsdevice, a control panel (e.g., for controlling and/or interacting withvarious components, features, and/or systems), and/or other components.In at least one embodiment, infotainment SoC 930 may further be used toprovide information (e.g., visual and/or audible) to user(s) of vehicle900, such as information from ADAS system 938, autonomous drivinginformation such as planned vehicle maneuvers, trajectories, surroundingenvironment information (e.g., intersection information, vehicleinformation, road information, etc.), and/or other information.

In at least one embodiment, infotainment SoC 930 may include any amountand type of GPU functionality. In at least one embodiment, infotainmentSoC 930 may communicate over bus 902 with other devices, systems, and/orcomponents of vehicle 900. In at least one embodiment, infotainment SoC930 may be coupled to a supervisory MCU such that a GPU of aninfotainment system may perform some self-driving functions in eventthat primary controller(s) 936 (e.g., primary and/or backup computers ofvehicle 900) fail. In at least one embodiment, infotainment SoC 930 mayput vehicle 900 into a chauffeur to safe stop mode, as described herein.

In at least one embodiment, vehicle 900 may further include instrumentcluster 932 (e.g., a digital dash, an electronic instrument cluster, adigital instrument panel, etc.). In at least one embodiment, instrumentcluster 932 may include, without limitation, a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). In atleast one embodiment, instrument cluster 932 may include, withoutlimitation, any number and combination of a set of instrumentation suchas a speedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s),supplemental restraint system (e.g., airbag) information, lightingcontrols, safety system controls, navigation information, etc. In someexamples, information may be displayed and/or shared among infotainmentSoC 930 and instrument cluster 932. In at least one embodiment,instrument cluster 932 may be included as part of infotainment SoC 930,or vice versa.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 9C for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

FIG. 9D is a diagram of a system 976 for communication betweencloud-based server(s) and autonomous vehicle 900 of FIG. 9A, accordingto at least one embodiment. In at least one embodiment, system 976 mayinclude, without limitation, server(s) 978, network(s) 990, and anynumber and type of vehicles, including vehicle 900. In at least oneembodiment, server(s) 978 may include, without limitation, a pluralityof GPUs 984(A)-984(H) (collectively referred to herein as GPUs 984),PCIe switches 982(A)-982(D) (collectively referred to herein as PCIeswitches 982), and/or CPUs 980(A)-980(B) (collectively referred toherein as CPUs 980). In at least one embodiment, GPUs 984, CPUs 980, andPCIe switches 982 may be interconnected with high-speed interconnectssuch as, for example and without limitation, NVLink interfaces 988developed by NVIDIA and/or PCIe connections 986. In at least oneembodiment, GPUs 984 are connected via an NVLink and/or NVSwitch SoC andGPUs 984 and PCIe switches 982 are connected via PCIe interconnects.Although eight GPUs 984, two CPUs 980, and four PCIe switches 982 areillustrated, this is not intended to be limiting. In at least oneembodiment, each of server(s) 978 may include, without limitation, anynumber of GPUs 984, CPUs 980, and/or PCIe switches 982, in anycombination. For example, in at least one embodiment, server(s) 978could each include eight, sixteen, thirty-two, and/or more GPUs 984.

In at least one embodiment, server(s) 978 may receive, over network(s)990 and from vehicles, image data representative of images showingunexpected or changed road conditions, such as recently commencedroad-work. In at least one embodiment, server(s) 978 may transmit, overnetwork(s) 990 and to vehicles, neural networks 992, updated orotherwise, and/or map information 994, including, without limitation,information regarding traffic and road conditions. In at least oneembodiment, updates to map information 994 may include, withoutlimitation, updates for HD map 922, such as information regardingconstruction sites, potholes, detours, flooding, and/or otherobstructions. In at least one embodiment, neural networks 992, and/ormap information 994 may have resulted from new training and/orexperiences represented in data received from any number of vehicles inan environment, and/or based at least in part on training performed at adata center (e.g., using server(s) 978 and/or other servers).

In at least one embodiment, server(s) 978 may be used to train machinelearning models (e.g., neural networks) based at least in part ontraining data. In at least one embodiment, training data may begenerated by vehicles, and/or may be generated in a simulation (e.g.,using a game engine). In at least one embodiment, any amount of trainingdata is tagged (e.g., where associated neural network benefits fromsupervised learning) and/or undergoes other pre-processing. In at leastone embodiment, any amount of training data is not tagged and/orpre-processed (e.g., where associated neural network does not requiresupervised learning). In at least one embodiment, once machine learningmodels are trained, machine learning models may be used by vehicles(e.g., transmitted to vehicles over network(s) 990), and/or machinelearning models may be used by server(s) 978 to remotely monitorvehicles.

In at least one embodiment, server(s) 978 may receive data from vehiclesand apply data to up-to-date real-time neural networks for real-timeintelligent inferencing. In at least one embodiment, server(s) 978 mayinclude deep-learning supercomputers and/or dedicated AI computerspowered by GPU(s) 984, such as a DGX and DGX Station machines developedby NVIDIA. However, in at least one embodiment, server(s) 978 mayinclude deep learning infrastructure that uses CPU-powered data centers.

In at least one embodiment, deep-learning infrastructure of server(s)978 may be capable of fast, real-time inferencing, and may use thatcapability to evaluate and verify health of processors, software, and/orassociated hardware in vehicle 900. For example, in at least oneembodiment, deep-learning infrastructure may receive periodic updatesfrom vehicle 900, such as a sequence of images and/or objects thatvehicle 900 has located in that sequence of images (e.g., via computervision and/or other machine learning object classification techniques).In at least one embodiment, deep-learning infrastructure may run its ownneural network to identify objects and compare them with objectsidentified by vehicle 900 and, if results do not match and deep-learninginfrastructure concludes that AI in vehicle 900 is malfunctioning, thenserver(s) 978 may transmit a signal to vehicle 900 instructing afail-safe computer of vehicle 900 to assume control, notify passengers,and complete a safe parking maneuver.

In at least one embodiment, server(s) 978 may include GPU(s) 984 and oneor more programmable inference accelerators (e.g., NVIDIA's TensorRT 3devices). In at least one embodiment, a combination of GPU-poweredservers and inference acceleration may make real-time responsivenesspossible. In at least one embodiment, such as where performance is lesscritical, servers powered by CPUs, FPGAs, and other processors may beused for inferencing. In at least one embodiment, hardware structure(s)615 are used to perform one or more embodiments. Details regardinghardware structure(s) 615 are provided herein in conjunction with FIGS.6A and/or 6B.

In at least one embodiment, one or more systems depicted in FIG. 9A-FIG.9D are utilized to implement an attention-on-label training process. Inat least one embodiment, one or more systems depicted in FIG. 9A-FIG. 9Dare utilized to implement one or more networks and training schemes suchas those described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 9A-FIG. 9D are utilizedto implement a learning method that utilizes meta-training withgradients from various label sets of training data to select a label forfinal gradient back-propagation.

Computer Systems

FIG. 10 is a block diagram illustrating an exemplary computer system,which may be a system with interconnected devices and components, asystem-on-a-chip (SOC) or some combination thereof formed with aprocessor that may include execution units to execute an instruction,according to at least one embodiment. In at least one embodiment, acomputer system 1000 may include, without limitation, a component, suchas a processor 1002 to employ execution units including logic to performalgorithms for process data, in accordance with present disclosure, suchas in embodiment described herein. In at least one embodiment, computersystem 1000 may include processors, such as PENTIUM® Processor family,Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel®Nervana™ microprocessors available from Intel Corporation of SantaClara, Calif., although other systems (including PCs having othermicroprocessors, engineering workstations, set-top boxes and like) mayalso be used. In at least one embodiment, computer system 1000 mayexecute a version of WINDOWS operating system available from MicrosoftCorporation of Redmond, Wash., although other operating systems (UNIXand Linux, for example), embedded software, and/or graphical userinterfaces, may also be used.

Embodiments may be used in other devices such as handheld devices andembedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (“PDAs”), and handheld PCs. In at least oneembodiment, embedded applications may include a microcontroller, adigital signal processor (“DSP”), system on a chip, network computers(“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”)switches, or any other system that may perform one or more instructionsin accordance with at least one embodiment.

In at least one embodiment, computer system 1000 may include, withoutlimitation, processor 1002 that may include, without limitation, one ormore execution units 1008 to perform machine learning model trainingand/or inferencing according to techniques described herein. In at leastone embodiment, computer system 1000 is a single processor desktop orserver system, but in another embodiment, computer system 1000 may be amultiprocessor system. In at least one embodiment, processor 1002 mayinclude, without limitation, a complex instruction set computer (“CISC”)microprocessor, a reduced instruction set computing (“RISC”)microprocessor, a very long instruction word (“VLIW”) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. In atleast one embodiment, processor 1002 may be coupled to a processor bus1010 that may transmit data signals between processor 1002 and othercomponents in computer system 1000.

In at least one embodiment, processor 1002 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 1004. In atleast one embodiment, processor 1002 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 1002. Other embodiments may alsoinclude a combination of both internal and external caches depending onparticular implementation and needs. In at least one embodiment, aregister file 1006 may store different types of data in variousregisters including, without limitation, integer registers, floatingpoint registers, status registers, and an instruction pointer register.

In at least one embodiment, execution unit 1008, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 1002. In at least one embodiment, processor 1002may also include a microcode (“ucode”) read only memory (“ROM”) thatstores microcode for certain macro instructions. In at least oneembodiment, execution unit 1008 may include logic to handle a packedinstruction set 1009. In at least one embodiment, by including packedinstruction set 1009 in an instruction set of a general-purposeprocessor, along with associated circuitry to execute instructions,operations used by many multimedia applications may be performed usingpacked data in processor 1002. In at least one embodiment, manymultimedia applications may be accelerated and executed more efficientlyby using a full width of a processor's data bus for performingoperations on packed data, which may eliminate a need to transfersmaller units of data across that processor's data bus to perform one ormore operations one data element at a time.

In at least one embodiment, execution unit 1008 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system1000 may include, without limitation, a memory 1020. In at least oneembodiment, memory 1020 may be a Dynamic Random Access Memory (“DRAM”)device, a Static Random Access Memory (“SRAM”) device, a flash memorydevice, or another memory device. In at least one embodiment, memory1020 may store instruction(s) 1019 and/or data 1021 represented by datasignals that may be executed by processor 1002.

In at least one embodiment, a system logic chip may be coupled toprocessor bus 1010 and memory 1020. In at least one embodiment, a systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 1016, and processor 1002 may communicate with MCH 1016 viaprocessor bus 1010. In at least one embodiment, MCH 1016 may provide ahigh bandwidth memory path 1018 to memory 1020 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 1016 may direct data signals between processor1002, memory 1020, and other components in computer system 1000 and tobridge data signals between processor bus 1010, memory 1020, and asystem I/O interface 1022. In at least one embodiment, a system logicchip may provide a graphics port for coupling to a graphics controller.In at least one embodiment, MCH 1016 may be coupled to memory 1020through high bandwidth memory path 1018 and a graphics/video card 1012may be coupled to MCH 1016 through an Accelerated Graphics Port (“AGP”)interconnect 1014.

In at least one embodiment, computer system 1000 may use system I/Ointerface 1022 as a proprietary hub interface bus to couple MCH 1016 toan I/O controller hub (“ICH”) 1030. In at least one embodiment, ICH 1030may provide direct connections to some I/O devices via a local I/O bus.In at least one embodiment, a local I/O bus may include, withoutlimitation, a high-speed I/O bus for connecting peripherals to memory1020, a chipset, and processor 1002. Examples may include, withoutlimitation, an audio controller 1029, a firmware hub (“flash BIOS”)1028, a wireless transceiver 1026, a data storage 1024, a legacy I/Ocontroller 1023 containing user input and keyboard interfaces 1025, aserial expansion port 1027, such as a Universal Serial Bus (“USB”) port,and a network controller 1034. In at least one embodiment, data storage1024 may comprise a hard disk drive, a floppy disk drive, a CD-ROMdevice, a flash memory device, or other mass storage device.

In at least one embodiment, FIG. 10 illustrates a system, which includesinterconnected hardware devices or “chips”, whereas in otherembodiments, FIG. 10 may illustrate an exemplary SoC. In at least oneembodiment, devices illustrated in FIG. 10 may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of computer system 1000 are interconnected using computeexpress link (CXL) interconnects.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 10 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 10 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 10 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 10 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 11 is a block diagram illustrating an electronic device 1100 forutilizing a processor 1110, according to at least one embodiment. In atleast one embodiment, electronic device 1100 may be, for example andwithout limitation, a notebook, a tower server, a rack server, a bladeserver, a laptop, a desktop, a tablet, a mobile device, a phone, anembedded computer, or any other suitable electronic device.

In at least one embodiment, electronic device 1100 may include, withoutlimitation, processor 1110 communicatively coupled to any suitablenumber or kind of components, peripherals, modules, or devices. In atleast one embodiment, processor 1110 is coupled using a bus orinterface, such as a I²C bus, a System Management Bus (“SMBus”), a LowPin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a HighDefinition Audio (“HDA”) bus, a Serial Advance Technology Attachment(“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3, etc.),or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In atleast one embodiment, FIG. 11 illustrates a system, which includesinterconnected hardware devices or “chips”, whereas in otherembodiments, FIG. 11 may illustrate an exemplary SoC. In at least oneembodiment, devices illustrated in FIG. 11 may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of FIG. 11 are interconnected using compute express link(CXL) interconnects.

In at least one embodiment, FIG. 11 may include a display 1124, a touchscreen 1125, a touch pad 1130, a Near Field Communications unit (“NFC”)1145, a sensor hub 1140, a thermal sensor 1146, an Express Chipset(“EC”) 1135, a Trusted Platform Module (“TPM”) 1138, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 1122, a DSP 1160, a drive 1120 such as a SolidState Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local areanetwork unit (“WLAN”) 1150, a Bluetooth unit 1152, a Wireless Wide AreaNetwork unit (“WWAN”) 1156, a Global Positioning System (GPS) unit 1155,a camera (“USB 3.0 camera”) 1154 such as a USB 3.0 camera, and/or a LowPower Double Data Rate (“LPDDR”) memory unit (“LPDDR3”) 1115 implementedin, for example, an LPDDR3 standard. These components may each beimplemented in any suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 1110 through components described herein. In atleast one embodiment, an accelerometer 1141, an ambient light sensor(“ALS”) 1142, a compass 1143, and a gyroscope 1144 may becommunicatively coupled to sensor hub 1140. In at least one embodiment,a thermal sensor 1139, a fan 1137, a keyboard 1136, and touch pad 1130may be communicatively coupled to EC 1135. In at least one embodiment,speakers 1163, headphones 1164, and a microphone (“mic”) 1165 may becommunicatively coupled to an audio unit (“audio codec and class D amp”)1162, which may in turn be communicatively coupled to DSP 1160. In atleast one embodiment, audio unit 1162 may include, for example andwithout limitation, an audio coder/decoder (“codec”) and a class Damplifier. In at least one embodiment, a SIM card (“SIM”) 1157 may becommunicatively coupled to WWAN unit 1156. In at least one embodiment,components such as WLAN unit 1150 and Bluetooth unit 1152, as well asWWAN unit 1156 may be implemented in a Next Generation Form Factor(“NGFF”).

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 11 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 11 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 11 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 11 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 12 illustrates a computer system 1200, according to at least oneembodiment. In at least one embodiment, computer system 1200 isconfigured to implement various processes and methods describedthroughout this disclosure.

In at least one embodiment, computer system 1200 comprises, withoutlimitation, at least one central processing unit (“CPU”) 1202 that isconnected to a communication bus 1210 implemented using any suitableprotocol, such as PCI (“Peripheral Component Interconnect”), peripheralcomponent interconnect express (“PCI-Express”), AGP (“AcceleratedGraphics Port”), HyperTransport, or any other bus or point-to-pointcommunication protocol(s). In at least one embodiment, computer system1200 includes, without limitation, a main memory 1204 and control logic(e.g., implemented as hardware, software, or a combination thereof) anddata are stored in main memory 1204, which may take form of randomaccess memory (“RAM”). In at least one embodiment, a network interfacesubsystem (“network interface”) 1222 provides an interface to othercomputing devices and networks for receiving data from and transmittingdata to other systems with computer system 1200.

In at least one embodiment, computer system 1200, in at least oneembodiment, includes, without limitation, input devices 1208, a parallelprocessing system 1212, and display devices 1206 that can be implementedusing a conventional cathode ray tube (“CRT”), a liquid crystal display(“LCD”), a light emitting diode (“LED”) display, a plasma display, orother suitable display technologies. In at least one embodiment, userinput is received from input devices 1208 such as keyboard, mouse,touchpad, microphone, etc. In at least one embodiment, each moduledescribed herein can be situated on a single semiconductor platform toform a processing system.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 12 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 12 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 12 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 12 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 13 illustrates a computer system 1300, according to at least oneembodiment. In at least one embodiment, computer system 1300 includes,without limitation, a computer 1310 and a USB stick 1320. In at leastone embodiment, computer 1310 may include, without limitation, anynumber and type of processor(s) (not shown) and a memory (not shown). Inat least one embodiment, computer 1310 includes, without limitation, aserver, a cloud instance, a laptop, and a desktop computer.

In at least one embodiment, USB stick 1320 includes, without limitation,a processing unit 1330, a USB interface 1340, and USB interface logic1350. In at least one embodiment, processing unit 1330 may be anyinstruction execution system, apparatus, or device capable of executinginstructions. In at least one embodiment, processing unit 1330 mayinclude, without limitation, any number and type of processing cores(not shown). In at least one embodiment, processing unit 1330 comprisesan application specific integrated circuit (“ASIC”) that is optimized toperform any amount and type of operations associated with machinelearning. For instance, in at least one embodiment, processing unit 1330is a tensor processing unit (“TPC”) that is optimized to perform machinelearning inference operations. In at least one embodiment, processingunit 1330 is a vision processing unit (“VPU”) that is optimized toperform machine vision and machine learning inference operations.

In at least one embodiment, USB interface 1340 may be any type of USBconnector or USB socket. For instance, in at least one embodiment, USBinterface 1340 is a USB 3.0 Type-C socket for data and power. In atleast one embodiment, USB interface 1340 is a USB 3.0 Type-A connector.In at least one embodiment, USB interface logic 1350 may include anyamount and type of logic that enables processing unit 1330 to interfacewith devices (e.g., computer 1310) via USB connector 1340.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 13 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 13 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 13 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 13 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 14A illustrates an exemplary architecture in which a plurality ofGPUs 1410(1)-1410(N) is communicatively coupled to a plurality ofmulti-core processors 1405(1)-1405(M) over high-speed links1440(1)-1440(N) (e.g., buses, point-to-point interconnects, etc.). In atleast one embodiment, high-speed links 1440(1)-1440(N) support acommunication throughput of 4 GB/s, 30 GB/s, 80 GB/s or higher. In atleast one embodiment, various interconnect protocols may be usedincluding, but not limited to, PCIe 4.0 or 5.0 and NVLink 2.0. Invarious figures, “N” and “M” represent positive integers, values ofwhich may be different from figure to figure.

In addition, and in at least one embodiment, two or more of GPUs 1410are interconnected over high-speed links 1429(1)-1429(2), which may beimplemented using similar or different protocols/links than those usedfor high-speed links 1440(1)-1440(N). Similarly, two or more ofmulti-core processors 1405 may be connected over a high-speed link 1428which may be symmetric multi-processor (SMP) buses operating at 20 GB/s,30 GB/s, 120 GB/s or higher. Alternatively, all communication betweenvarious system components shown in FIG. 14A may be accomplished usingsimilar protocols/links (e.g., over a common interconnection fabric).

In at least one embodiment, each multi-core processor 1405 iscommunicatively coupled to a processor memory 1401(1)-1401(M), viamemory interconnects 1426(1)-1426(M), respectively, and each GPU1410(1)-1410(N) is communicatively coupled to GPU memory 1420(1)-1420(N)over GPU memory interconnects 1450(1)-1450(N), respectively. In at leastone embodiment, memory interconnects 1426 and 1450 may utilize similaror different memory access technologies. By way of example, and notlimitation, processor memories 1401(1)-1401(M) and GPU memories 1420 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In at least one embodiment, someportion of processor memories 1401 may be volatile memory and anotherportion may be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described herein, although various multi-core processors 1405 andGPUs 1410 may be physically coupled to a particular memory 1401, 1420,respectively, and/or a unified memory architecture may be implemented inwhich a virtual system address space (also referred to as “effectiveaddress” space) is distributed among various physical memories. Forexample, processor memories 1401(1)-1401(M) may each comprise 64 GB ofsystem memory address space and GPU memories 1420(1)-1420(N) may eachcomprise 32 GB of system memory address space resulting in a total of256 GB addressable memory when M=2 and N=4. Other values for N and M arepossible.

FIG. 14B illustrates additional details for an interconnection between amulti-core processor 1407 and a graphics acceleration module 1446 inaccordance with one exemplary embodiment. In at least one embodiment,graphics acceleration module 1446 may include one or more GPU chipsintegrated on a line card which is coupled to processor 1407 viahigh-speed link 1440 (e.g., a PCIe bus, NVLink, etc.). In at least oneembodiment, graphics acceleration module 1446 may alternatively beintegrated on a package or chip with processor 1407.

In at least one embodiment, processor 1407 includes a plurality of cores1460A-1460D, each with a translation lookaside buffer (“TLB”)1461A-1461D and one or more caches 1462A-1462D. In at least oneembodiment, cores 1460A-1460D may include various other components forexecuting instructions and processing data that are not illustrated. Inat least one embodiment, caches 1462A-1462D may comprise Level 1 (L1)and Level 2 (L2) caches. In addition, one or more shared caches 1456 maybe included in caches 1462A-1462D and shared by sets of cores1460A-1460D. For example, one embodiment of processor 1407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one or more L2 and L3 caches areshared by two adjacent cores. In at least one embodiment, processor 1407and graphics acceleration module 1446 connect with system memory 1414,which may include processor memories 1401(1)-1401(M) of FIG. 14A.

In at least one embodiment, coherency is maintained for data andinstructions stored in various caches 1462A-1462D, 1456 and systemmemory 1414 via inter-core communication over a coherence bus 1464. Inat least one embodiment, for example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overcoherence bus 1464 in response to detected reads or writes to particularcache lines. In at least one embodiment, a cache snooping protocol isimplemented over coherence bus 1464 to snoop cache accesses.

In at least one embodiment, a proxy circuit 1425 communicatively couplesgraphics acceleration module 1446 to coherence bus 1464, allowinggraphics acceleration module 1446 to participate in a cache coherenceprotocol as a peer of cores 1460A-1460D. In particular, in at least oneembodiment, an interface 1435 provides connectivity to proxy circuit1425 over high-speed link 1440 and an interface 1437 connects graphicsacceleration module 1446 to high-speed link 1440.

In at least one embodiment, an accelerator integration circuit 1436provides cache management, memory access, context management, andinterrupt management services on behalf of a plurality of graphicsprocessing engines 1431(1)-1431(N) of graphics acceleration module 1446.In at least one embodiment, graphics processing engines 1431(1)-1431(N)may each comprise a separate graphics processing unit (GPU). In at leastone embodiment, graphics processing engines 1431(1)-1431(N)alternatively may comprise different types of graphics processingengines within a GPU, such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inat least one embodiment, graphics acceleration module 1446 may be a GPUwith a plurality of graphics processing engines 1431(1)-1431(N) orgraphics processing engines 1431(1)-1431(N) may be individual GPUsintegrated on a common package, line card, or chip.

In at least one embodiment, accelerator integration circuit 1436includes a memory management unit (MMU) 1439 for performing variousmemory management functions such as virtual-to-physical memorytranslations (also referred to as effective-to-real memory translations)and memory access protocols for accessing system memory 1414. In atleast one embodiment, MMU 1439 may also include a translation lookasidebuffer (TLB) (not shown) for caching virtual/effective to physical/realaddress translations. In at least one embodiment, a cache 1438 can storecommands and data for efficient access by graphics processing engines1431(1)-1431(N). In at least one embodiment, data stored in cache 1438and graphics memories 1433(1)-1433(M) is kept coherent with core caches1462A-1462D, 1456 and system memory 1414, possibly using a fetch unit1444. As mentioned, this may be accomplished via proxy circuit 1425 onbehalf of cache 1438 and memories 1433(1)-1433(M) (e.g., sending updatesto cache 1438 related to modifications/accesses of cache lines onprocessor caches 1462A-1462D, 1456 and receiving updates from cache1438).

In at least one embodiment, a set of registers 1445 store context datafor threads executed by graphics processing engines 1431(1)-1431(N) anda context management circuit 1448 manages thread contexts. For example,context management circuit 1448 may perform save and restore operationsto save and restore contexts of various threads during contexts switches(e.g., where a first thread is saved and a second thread is stored sothat a second thread can be execute by a graphics processing engine).For example, on a context switch, context management circuit 1448 maystore current register values to a designated region in memory (e.g.,identified by a context pointer). It may then restore register valueswhen returning to a context. In at least one embodiment, an interruptmanagement circuit 1447 receives and processes interrupts received fromsystem devices.

In at least one embodiment, virtual/effective addresses from a graphicsprocessing engine 1431 are translated to real/physical addresses insystem memory 1414 by MMU 1439. In at least one embodiment, acceleratorintegration circuit 1436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 1446 and/or other accelerator devices. In at leastone embodiment, graphics accelerator module 1446 may be dedicated to asingle application executed on processor 1407 or may be shared betweenmultiple applications. In at least one embodiment, a virtualizedgraphics execution environment is presented in which resources ofgraphics processing engines 1431(1)-1431(N) are shared with multipleapplications or virtual machines (VMs). In at least one embodiment,resources may be subdivided into “slices” which are allocated todifferent VMs and/or applications based on processing requirements andpriorities associated with VMs and/or applications.

In at least one embodiment, accelerator integration circuit 1436performs as a bridge to a system for graphics acceleration module 1446and provides address translation and system memory cache services. Inaddition, in at least one embodiment, accelerator integration circuit1436 may provide virtualization facilities for a host processor tomanage virtualization of graphics processing engines 1431(1)-1431(N),interrupts, and memory management.

In at least one embodiment, because hardware resources of graphicsprocessing engines 1431(1)-1431(N) are mapped explicitly to a realaddress space seen by host processor 1407, any host processor canaddress these resources directly using an effective address value. In atleast one embodiment, one function of accelerator integration circuit1436 is physical separation of graphics processing engines1431(1)-1431(N) so that they appear to a system as independent units.

In at least one embodiment, one or more graphics memories1433(1)-1433(M) are coupled to each of graphics processing engines1431(1)-1431(N), respectively and N=M. In at least one embodiment,graphics memories 1433(1)-1433(M) store instructions and data beingprocessed by each of graphics processing engines 1431(1)-1431(N). In atleast one embodiment, graphics memories 1433(1)-1433(M) may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In at least one embodiment, to reduce data traffic over high-speed link1440, biasing techniques can be used to ensure that data stored ingraphics memories 1433(1)-1433(M) is data that will be used mostfrequently by graphics processing engines 1431(1)-1431(N) and preferablynot used by cores 1460A-1460D (at least not frequently). Similarly, inat least one embodiment, a biasing mechanism attempts to keep dataneeded by cores (and preferably not graphics processing engines1431(1)-1431(N)) within caches 1462A-1462D, 1456 and system memory 1414.

FIG. 14C illustrates another exemplary embodiment in which acceleratorintegration circuit 1436 is integrated within processor 1407. In thisembodiment, graphics processing engines 1431(1)-1431(N) communicatedirectly over high-speed link 1440 to accelerator integration circuit1436 via interface 1437 and interface 1435 (which, again, may be anyform of bus or interface protocol). In at least one embodiment,accelerator integration circuit 1436 may perform similar operations asthose described with respect to FIG. 14B, but potentially at a higherthroughput given its close proximity to coherence bus 1464 and caches1462A-1462D, 1456. In at least one embodiment, an acceleratorintegration circuit supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization),which may include programming models which are controlled by acceleratorintegration circuit 1436 and programming models which are controlled bygraphics acceleration module 1446.

In at least one embodiment, graphics processing engines 1431(1)-1431(N)are dedicated to a single application or process under a singleoperating system. In at least one embodiment, a single application canfunnel other application requests to graphics processing engines1431(1)-1431(N), providing virtualization within a VM/partition.

In at least one embodiment, graphics processing engines 1431(1)-1431(N),may be shared by multiple VM/application partitions. In at least oneembodiment, shared models may use a system hypervisor to virtualizegraphics processing engines 1431(1)-1431(N) to allow access by eachoperating system. In at least one embodiment, for single-partitionsystems without a hypervisor, graphics processing engines1431(1)-1431(N) are owned by an operating system. In at least oneembodiment, an operating system can virtualize graphics processingengines 1431(1)-1431(N) to provide access to each process orapplication.

In at least one embodiment, graphics acceleration module 1446 or anindividual graphics processing engine 1431(1)-1431(N) selects a processelement using a process handle. In at least one embodiment, processelements are stored in system memory 1414 and are addressable using aneffective address to real address translation technique describedherein. In at least one embodiment, a process handle may be animplementation-specific value provided to a host process whenregistering its context with graphics processing engine 1431(1)-1431(N)(that is, calling system software to add a process element to a processelement linked list). In at least one embodiment, a lower 16-bits of aprocess handle may be an offset of a process element within a processelement linked list.

FIG. 14D illustrates an exemplary accelerator integration slice 1490. Inat least one embodiment, a “slice” comprises a specified portion ofprocessing resources of accelerator integration circuit 1436. In atleast one embodiment, an application is effective address space 1482within system memory 1414 stores process elements 1483. In at least oneembodiment, process elements 1483 are stored in response to GPUinvocations 1481 from applications 1480 executed on processor 1407. Inat least one embodiment, a process element 1483 contains process statefor corresponding application 1480. In at least one embodiment, a workdescriptor (WD) 1484 contained in process element 1483 can be a singlejob requested by an application or may contain a pointer to a queue ofjobs. In at least one embodiment, WD 1484 is a pointer to a job requestqueue in an application's effective address space 1482.

In at least one embodiment, graphics acceleration module 1446 and/orindividual graphics processing engines 1431(1)-1431(N) can be shared byall or a subset of processes in a system. In at least one embodiment, aninfrastructure for setting up process states and sending a WD 1484 to agraphics acceleration module 1446 to start a job in a virtualizedenvironment may be included.

In at least one embodiment, a dedicated-process programming model isimplementation-specific. In at least one embodiment, in this model, asingle process owns graphics acceleration module 1446 or an individualgraphics processing engine 1431. In at least one embodiment, whengraphics acceleration module 1446 is owned by a single process, ahypervisor initializes accelerator integration circuit 1436 for anowning partition and an operating system initializes acceleratorintegration circuit 1436 for an owning process when graphicsacceleration module 1446 is assigned.

In at least one embodiment, in operation, a WD fetch unit 1491 inaccelerator integration slice 1490 fetches next WD 1484, which includesan indication of work to be done by one or more graphics processingengines of graphics acceleration module 1446. In at least oneembodiment, data from WD 1484 may be stored in registers 1445 and usedby MMU 1439, interrupt management circuit 1447 and/or context managementcircuit 1448 as illustrated. For example, one embodiment of MMU 1439includes segment/page walk circuitry for accessing segment/page tables1486 within an OS virtual address space 1485. In at least oneembodiment, interrupt management circuit 1447 may process interruptevents 1492 received from graphics acceleration module 1446. In at leastone embodiment, when performing graphics operations, an effectiveaddress 1493 generated by a graphics processing engine 1431(1)-1431(N)is translated to a real address by MMU 1439.

In at least one embodiment, registers 1445 are duplicated for eachgraphics processing engine 1431(1)-1431(N) and/or graphics accelerationmodule 1446 and may be initialized by a hypervisor or an operatingsystem. In at least one embodiment, each of these duplicated registersmay be included in an accelerator integration slice 1490. Exemplaryregisters that may be initialized by a hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers Register # Description 1 SliceControl Register 2 Real Address (RA) Scheduled Processes Area Pointer 3Authority Mask Override Register 4 Interrupt Vector Table Entry Offset 5Interrupt Vector Table Entry Limit 6 State Register 7 Logical PartitionID 8 Real address (RA) Hypervisor Accelerator Utilization Record Pointer9 Storage Description Register

Exemplary registers that may be initialized by an operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers Register # Description 1Process and Thread Identification 2 Effective Address (EA) ContextSave/Restore Pointer 3 Virtual Address (VA) Accelerator UtilizationRecord Pointer 4 Virtual Address (VA) Storage Segment Table Pointer 5Authority Mask 6 Work descriptor

In at least one embodiment, each WD 1484 is specific to a particulargraphics acceleration module 1446 and/or graphics processing engines1431(1)-1431(N). In at least one embodiment, it contains all informationrequired by a graphics processing engine 1431(1)-1431(N) to do work, orit can be a pointer to a memory location where an application has set upa command queue of work to be completed.

FIG. 14E illustrates additional details for one exemplary embodiment ofa shared model. This embodiment includes a hypervisor real address space1498 in which a process element list 1499 is stored. In at least oneembodiment, hypervisor real address space 1498 is accessible via ahypervisor 1496 which virtualizes graphics acceleration module enginesfor operating system 1495.

In at least one embodiment, shared programming models allow for all or asubset of processes from all or a subset of partitions in a system touse a graphics acceleration module 1446. In at least one embodiment,there are two programming models where graphics acceleration module 1446is shared by multiple processes and partitions, namely time-slicedshared and graphics directed shared.

In at least one embodiment, in this model, system hypervisor 1496 ownsgraphics acceleration module 1446 and makes its function available toall operating systems 1495. In at least one embodiment, for a graphicsacceleration module 1446 to support virtualization by system hypervisor1496, graphics acceleration module 1446 may adhere to certainrequirements, such as (1) an application's job request must beautonomous (that is, state does not need to be maintained between jobs),or graphics acceleration module 1446 must provide a context save andrestore mechanism, (2) an application's job request is guaranteed bygraphics acceleration module 1446 to complete in a specified amount oftime, including any translation faults, or graphics acceleration module1446 provides an ability to preempt processing of a job, and (3)graphics acceleration module 1446 must be guaranteed fairness betweenprocesses when operating in a directed shared programming model.

In at least one embodiment, application 1480 is required to make anoperating system 1495 system call with a graphics acceleration moduletype, a work descriptor (WD), an authority mask register (AMR) value,and a context save/restore area pointer (CSRP). In at least oneembodiment, graphics acceleration module type describes a targetedacceleration function for a system call. In at least one embodiment,graphics acceleration module type may be a system-specific value. In atleast one embodiment, WD is formatted specifically for graphicsacceleration module 1446 and can be in a form of a graphics accelerationmodule 1446 command, an effective address pointer to a user-definedstructure, an effective address pointer to a queue of commands, or anyother data structure to describe work to be done by graphicsacceleration module 1446.

In at least one embodiment, an AMR value is an AMR state to use for acurrent process. In at least one embodiment, a value passed to anoperating system is similar to an application setting an AMR. In atleast one embodiment, if accelerator integration circuit 1436 (notshown) and graphics acceleration module 1446 implementations do notsupport a User Authority Mask Override Register (UAMOR), an operatingsystem may apply a current UAMOR value to an AMR value before passing anAMR in a hypervisor call. In at least one embodiment, hypervisor 1496may optionally apply a current Authority Mask Override Register (AMOR)value before placing an AMR into process element 1483. In at least oneembodiment, CSRP is one of registers 1445 containing an effectiveaddress of an area in an application's effective address space 1482 forgraphics acceleration module 1446 to save and restore context state. Inat least one embodiment, this pointer is optional if no state isrequired to be saved between jobs or when a job is preempted. In atleast one embodiment, context save/restore area may be pinned systemmemory.

Upon receiving a system call, operating system 1495 may verify thatapplication 1480 has registered and been given authority to use graphicsacceleration module 1446. In at least one embodiment, operating system1495 then calls hypervisor 1496 with information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters Parameter # Description 1 Awork descriptor (WD) 2 An Authority Mask Register (AMR) value(potentially masked) 3 An effective address (EA) Context Save/RestoreArea Pointer (CSRP) 4 A process ID (PID) and optional thread ID (TID) 5A virtual address (VA) accelerator utilization record pointer (AURP) 6Virtual address of storage segment table pointer (SSTP) 7 A logicalinterrupt service number (LISN)

In at least one embodiment, upon receiving a hypervisor call, hypervisor1496 verifies that operating system 1495 has registered and been givenauthority to use graphics acceleration module 1446. In at least oneembodiment, hypervisor 1496 then puts process element 1483 into aprocess element linked list for a corresponding graphics accelerationmodule 1446 type. In at least one embodiment, a process element mayinclude information shown in Table 4.

TABLE 4 Process Element Information Element # Description 1 A workdescriptor (WD) 2 An Authority Mask Register (AMR) value (potentiallymasked). 3 An effective address (EA) Context Save/Restore Area Pointer(CSRP) 4 A process ID (PID) and optional thread ID (TID) 5 A virtualaddress (VA) accelerator utilization record pointer (AURP) 6 Virtualaddress of storage segment table pointer (SSTP) 7 A logical interruptservice number (LISN) 8 Interrupt vector table, derived from hypervisorcall parameters 9 A state register (SR) value 10 A logical partition ID(LPID) 11 A real address (RA) hypervisor accelerator utilization recordpointer 12 Storage Descriptor Register (SDR)

In at least one embodiment, hypervisor initializes a plurality ofaccelerator integration slice 1490 registers 1445.

As illustrated in FIG. 14F, in at least one embodiment, a unified memoryis used, addressable via a common virtual memory address space used toaccess physical processor memories 1401(1)-1401(N) and GPU memories1420(1)-1420(N). In this implementation, operations executed on GPUs1410(1)-1410(N) utilize a same virtual/effective memory address space toaccess processor memories 1401(1)-1401(M) and vice versa, therebysimplifying programmability. In at least one embodiment, a first portionof a virtual/effective address space is allocated to processor memory1401(1), a second portion to second processor memory 1401(N), a thirdportion to GPU memory 1420(1), and so on. In at least one embodiment, anentire virtual/effective memory space (sometimes referred to as aneffective address space) is thereby distributed across each of processormemories 1401 and GPU memories 1420, allowing any processor or GPU toaccess any physical memory with a virtual address mapped to that memory.

In at least one embodiment, bias/coherence management circuitry1494A-1494E within one or more of MMUs 1439A-1439E ensures cachecoherence between caches of one or more host processors (e.g., 1405) andGPUs 1410 and implements biasing techniques indicating physical memoriesin which certain types of data should be stored. In at least oneembodiment, while multiple instances of bias/coherence managementcircuitry 1494A-1494E are illustrated in FIG. 14F, bias/coherencecircuitry may be implemented within an MMU of one or more hostprocessors 1405 and/or within accelerator integration circuit 1436.

One embodiment allows GPU memories 1420 to be mapped as part of systemmemory, and accessed using shared virtual memory (SVM) technology, butwithout suffering performance drawbacks associated with full systemcache coherence. In at least one embodiment, an ability for GPU memories1420 to be accessed as system memory without onerous cache coherenceoverhead provides a beneficial operating environment for GPU offload. Inat least one embodiment, this arrangement allows software of hostprocessor 1405 to setup operands and access computation results, withoutoverhead of tradition I/O DMA data copies. In at least one embodiment,such traditional copies involve driver calls, interrupts and memorymapped I/O (MMIO) accesses that are all inefficient relative to simplememory accesses. In at least one embodiment, an ability to access GPUmemories 1420 without cache coherence overheads can be critical toexecution time of an offloaded computation. In at least one embodiment,in cases with substantial streaming write memory traffic, for example,cache coherence overhead can significantly reduce an effective writebandwidth seen by a GPU 1410. In at least one embodiment, efficiency ofoperand setup, efficiency of results access, and efficiency of GPUcomputation may play a role in determining effectiveness of a GPUoffload.

In at least one embodiment, selection of GPU bias and host processorbias is driven by a bias tracker data structure. In at least oneembodiment, a bias table may be used, for example, which may be apage-granular structure (e.g., controlled at a granularity of a memorypage) that includes 1 or 2 bits per GPU-attached memory page. In atleast one embodiment, a bias table may be implemented in a stolen memoryrange of one or more GPU memories 1420, with or without a bias cache ina GPU 1410 (e.g., to cache frequently/recently used entries of a biastable). Alternatively, in at least one embodiment, an entire bias tablemay be maintained within a GPU.

In at least one embodiment, a bias table entry associated with eachaccess to a GPU attached memory 1420 is accessed prior to actual accessto a GPU memory, causing following operations. In at least oneembodiment, local requests from a GPU 1410 that find their page in GPUbias are forwarded directly to a corresponding GPU memory 1420. In atleast one embodiment, local requests from a GPU that find their page inhost bias are forwarded to processor 1405 (e.g., over a high-speed linkas described herein). In at least one embodiment, requests fromprocessor 1405 that find a requested page in host processor biascomplete a request like a normal memory read. Alternatively, requestsdirected to a GPU-biased page may be forwarded to a GPU 1410. In atleast one embodiment, a GPU may then transition a page to a hostprocessor bias if it is not currently using a page. In at least oneembodiment, a bias state of a page can be changed either by asoftware-based mechanism, a hardware-assisted software-based mechanism,or, for a limited set of cases, a purely hardware-based mechanism.

In at least one embodiment, one mechanism for changing bias stateemploys an API call (e.g., OpenCL), which, in turn, calls a GPU's devicedriver which, in turn, sends a message (or enqueues a commanddescriptor) to a GPU directing it to change a bias state and, for sometransitions, perform a cache flushing operation in a host. In at leastone embodiment, a cache flushing operation is used for a transition fromhost processor 1405 bias to GPU bias, but is not for an oppositetransition.

In at least one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by host processor 1405. In atleast one embodiment, to access these pages, processor 1405 may requestaccess from GPU 1410, which may or may not grant access right away. Inat least one embodiment, thus, to reduce communication between processor1405 and GPU 1410 it is beneficial to ensure that GPU-biased pages arethose which are required by a GPU but not host processor 1405 and viceversa.

Hardware structure(s) 615 are used to perform one or more embodiments.Details regarding a hardware structure(s) 615 may be provided herein inconjunction with FIGS. 6A and/or 6B.

In at least one embodiment, one or more systems depicted in FIG.14A-FIG. 14F are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 14A-FIG. 14F are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.14A-FIG. 14F are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIG. 15 illustrates exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included in at least oneembodiment, including additional graphics processors/cores, peripheralinterface controllers, or general-purpose processor cores.

FIG. 15 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1500 that may be fabricated using one or more IPcores, according to at least one embodiment. In at least one embodiment,integrated circuit 1500 includes one or more application processor(s)1505 (e.g., CPUs), at least one graphics processor 1510, and mayadditionally include an image processor 1515 and/or a video processor1520, any of which may be a modular IP core. In at least one embodiment,integrated circuit 1500 includes peripheral or bus logic including a USBcontroller 1525, a UART controller 1530, an SPI/SDIO controller 1535,and an I²2S/I²2C controller 1540. In at least one embodiment, integratedcircuit 1500 can include a display device 1545 coupled to one or more ofa high-definition multimedia interface (HDMI) controller 1550 and amobile industry processor interface (MIPI) display interface 1555. In atleast one embodiment, storage may be provided by a flash memorysubsystem 1560 including flash memory and a flash memory controller. Inat least one embodiment, a memory interface may be provided via a memorycontroller 1565 for access to SDRAM or SRAM memory devices. In at leastone embodiment, some integrated circuits additionally include anembedded security engine 1570.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used inintegrated circuit 1500 for inferencing or predicting operations based,at least in part, on weight parameters calculated using neural networktraining operations, neural network functions and/or architectures, orneural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 15 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 15 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 15 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIGS. 16A-16B illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included in at least oneembodiment, including additional graphics processors/cores, peripheralinterface controllers, or general-purpose processor cores.

FIGS. 16A-16B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 16A illustrates an exemplary graphics processor 1610 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to at least one embodiment. FIG. 16Billustrates an additional exemplary graphics processor 1640 of a systemon a chip integrated circuit that may be fabricated using one or more IPcores, according to at least one embodiment. In at least one embodiment,graphics processor 1610 of FIG. 16A is a low power graphics processorcore. In at least one embodiment, graphics processor 1640 of FIG. 16B isa higher performance graphics processor core. In at least oneembodiment, each of graphics processors 1610, 1640 can be variants ofgraphics processor 1510 of FIG. 15.

In at least one embodiment, graphics processor 1610 includes a vertexprocessor 1605 and one or more fragment processor(s) 1615A-1615N (e.g.,1615A, 1615B, 1615C, 1615D, through 1615N-1, and 1615N). In at least oneembodiment, graphics processor 1610 can execute different shaderprograms via separate logic, such that vertex processor 1605 isoptimized to execute operations for vertex shader programs, while one ormore fragment processor(s) 1615A-1615N execute fragment (e.g., pixel)shading operations for fragment or pixel shader programs. In at leastone embodiment, vertex processor 1605 performs a vertex processing stageof a 3D graphics pipeline and generates primitives and vertex data. Inat least one embodiment, fragment processor(s) 1615A-1615N use primitiveand vertex data generated by vertex processor 1605 to produce aframebuffer that is displayed on a display device. In at least oneembodiment, fragment processor(s) 1615A-1615N are optimized to executefragment shader programs as provided for in an OpenGL API, which may beused to perform similar operations as a pixel shader program as providedfor in a Direct 3D API.

In at least one embodiment, graphics processor 1610 additionallyincludes one or more memory management units (MMUs) 1620A-1620B,cache(s) 1625A-1625B, and circuit interconnect(s) 1630A-1630B. In atleast one embodiment, one or more MMU(s) 1620A-1620B provide for virtualto physical address mapping for graphics processor 1610, including forvertex processor 1605 and/or fragment processor(s) 1615A-1615N, whichmay reference vertex or image/texture data stored in memory, in additionto vertex or image/texture data stored in one or more cache(s)1625A-1625B. In at least one embodiment, one or more MMU(s) 1620A-1620Bmay be synchronized with other MMUs within a system, including one ormore MMUs associated with one or more application processor(s) 1505,image processors 1515, and/or video processors 1520 of FIG. 15, suchthat each processor 1505-1520 can participate in a shared or unifiedvirtual memory system. In at least one embodiment, one or more circuitinterconnect(s) 1630A-1630B enable graphics processor 1610 to interfacewith other IP cores within SoC, either via an internal bus of SoC or viaa direct connection.

In at least one embodiment, graphics processor 1640 includes one or moreshader core(s) 1655A-1655N (e.g., 1655A, 1655B, 1655C, 1655D, 1655E,1655F, through 1655N−1, and 1655N) as shown in FIG. 16B, which providesfor a unified shader core architecture in which a single core or type orcore can execute all types of programmable shader code, including shaderprogram code to implement vertex shaders, fragment shaders, and/orcompute shaders. In at least one embodiment, a number of shader corescan vary. In at least one embodiment, graphics processor 1640 includesan inter-core task manager 1645, which acts as a thread dispatcher todispatch execution threads to one or more shader cores 1655A-1655N and atiling unit 1658 to accelerate tiling operations for tile-basedrendering, in which rendering operations for a scene are subdivided inimage space, for example to exploit local spatial coherence within ascene or to optimize use of internal caches.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used inintegrated circuit 16A and/or 16B for inferencing or predictingoperations based, at least in part, on weight parameters calculatedusing neural network training operations, neural network functionsand/or architectures, or neural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG.16A-FIG. 16B are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 16A-FIG. 16B are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.16A-FIG. 16B are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIGS. 17A-17B illustrate additional exemplary graphics processor logicaccording to embodiments described herein. FIG. 17A illustrates agraphics core 1700 that may be included within graphics processor 1510of FIG. 15, in at least one embodiment, and may be a unified shader core1655A-1655N as in FIG. 16B in at least one embodiment. FIG. 17Billustrates a highly-parallel general-purpose graphics processing unit(“GPGPU”) 1730 suitable for deployment on a multi-chip module in atleast one embodiment.

In at least one embodiment, graphics core 1700 includes a sharedinstruction cache 1702, a texture unit 1718, and a cache/shared memory1720 that are common to execution resources within graphics core 1700.In at least one embodiment, graphics core 1700 can include multipleslices 1701A-1701N or a partition for each core, and a graphicsprocessor can include multiple instances of graphics core 1700. In atleast one embodiment, slices 1701A-1701N can include support logicincluding a local instruction cache 1704A-1704N, a thread scheduler1706A-1706N, a thread dispatcher 1708A-1708N, and a set of registers1710A-1710N. In at least one embodiment, slices 1701A-1701N can includea set of additional function units (AFUs 1712A-1712N), floating-pointunits (FPUs 1714A-1714N), integer arithmetic logic units (ALUs1716A-1716N), address computational units (ACUs 1713A-1713N),double-precision floating-point units (DPFPUs 1715A-1715N), and matrixprocessing units (MPUs 1717A-1717N).

In at least one embodiment, FPUs 1714A-1714N can performsingle-precision (32-bit) and half-precision (16-bit) floating pointoperations, while DPFPUs 1715A-1715N perform double precision (64-bit)floating point operations. In at least one embodiment, ALUs 1716A-1716Ncan perform variable precision integer operations at 8-bit, 16-bit, and32-bit precision, and can be configured for mixed precision operations.In at least one embodiment, MPUs 1717A-1717N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. In at least one embodiment, MPUs1717-1717N can perform a variety of matrix operations to acceleratemachine learning application frameworks, including enabling support foraccelerated general matrix to matrix multiplication (GEMM). In at leastone embodiment, AFUs 1712A-1712N can perform additional logic operationsnot supported by floating-point or integer units, includingtrigonometric operations (e.g., sine, cosine, etc.).

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in graphicscore 1700 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

FIG. 17B illustrates a general-purpose processing unit (GPGPU) 1730 thatcan be configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units, in at least oneembodiment. In at least one embodiment, GPGPU 1730 can be linkeddirectly to other instances of GPGPU 1730 to create a multi-GPU clusterto improve training speed for deep neural networks. In at least oneembodiment, GPGPU 1730 includes a host interface 1732 to enable aconnection with a host processor. In at least one embodiment, hostinterface 1732 is a PCI Express interface. In at least one embodiment,host interface 1732 can be a vendor-specific communications interface orcommunications fabric. In at least one embodiment, GPGPU 1730 receivescommands from a host processor and uses a global scheduler 1734 todistribute execution threads associated with those commands to a set ofcompute clusters 1736A-1736H. In at least one embodiment, computeclusters 1736A-1736H share a cache memory 1738. In at least oneembodiment, cache memory 1738 can serve as a higher-level cache forcache memories within compute clusters 1736A-1736H.

In at least one embodiment, GPGPU 1730 includes memory 1744A-1744Bcoupled with compute clusters 1736A-1736H via a set of memorycontrollers 1742A-1742B. In at least one embodiment, memory 1744A-1744Bcan include various types of memory devices including dynamic randomaccess memory (DRAM) or graphics random access memory, such assynchronous graphics random access memory (SGRAM), including graphicsdouble data rate (GDDR) memory.

In at least one embodiment, compute clusters 1736A-1736H each include aset of graphics cores, such as graphics core 1700 of FIG. 17A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example, in at least oneembodiment, at least a subset of floating point units in each of computeclusters 1736A-1736H can be configured to perform 16-bit or 32-bitfloating point operations, while a different subset of floating pointunits can be configured to perform 64-bit floating point operations.

In at least one embodiment, multiple instances of GPGPU 1730 can beconfigured to operate as a compute cluster. In at least one embodiment,communication used by compute clusters 1736A-1736H for synchronizationand data exchange varies across embodiments. In at least one embodiment,multiple instances of GPGPU 1730 communicate over host interface 1732.In at least one embodiment, GPGPU 1730 includes an I/O hub 1739 thatcouples GPGPU 1730 with a GPU link 1740 that enables a direct connectionto other instances of GPGPU 1730. In at least one embodiment, GPU link1740 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of GPGPU1730. In at least one embodiment, GPU link 1740 couples with ahigh-speed interconnect to transmit and receive data to other GPGPUs orparallel processors. In at least one embodiment, multiple instances ofGPGPU 1730 are located in separate data processing systems andcommunicate via a network device that is accessible via host interface1732. In at least one embodiment GPU link 1740 can be configured toenable a connection to a host processor in addition to or as analternative to host interface 1732.

In at least one embodiment, GPGPU 1730 can be configured to train neuralnetworks. In at least one embodiment, GPGPU 1730 can be used within aninferencing platform. In at least one embodiment, in which GPGPU 1730 isused for inferencing, GPGPU 1730 may include fewer compute clusters1736A-1736H relative to when GPGPU 1730 is used for training a neuralnetwork. In at least one embodiment, memory technology associated withmemory 1744A-1744B may differ between inferencing and trainingconfigurations, with higher bandwidth memory technologies devoted totraining configurations. In at least one embodiment, an inferencingconfiguration of GPGPU 1730 can support inferencing specificinstructions. For example, in at least one embodiment, an inferencingconfiguration can provide support for one or more 8-bit integer dotproduct instructions, which may be used during inferencing operationsfor deployed neural networks.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in GPGPU1730 for inferencing or predicting operations based, at least in part,on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG.17A-FIG. 17B are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 17A-FIG. 17B are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.17A-FIG. 17B are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIG. 18 is a block diagram illustrating a computing system 1800according to at least one embodiment. In at least one embodiment,computing system 1800 includes a processing subsystem 1801 having one ormore processor(s) 1802 and a system memory 1804 communicating via aninterconnection path that may include a memory hub 1805. In at least oneembodiment, memory hub 1805 may be a separate component within a chipsetcomponent or may be integrated within one or more processor(s) 1802. Inat least one embodiment, memory hub 1805 couples with an I/O subsystem1811 via a communication link 1806. In at least one embodiment, I/Osubsystem 1811 includes an I/O hub 1807 that can enable computing system1800 to receive input from one or more input device(s) 1808. In at leastone embodiment, I/O hub 1807 can enable a display controller, which maybe included in one or more processor(s) 1802, to provide outputs to oneor more display device(s) 1810A. In at least one embodiment, one or moredisplay device(s) 1810A coupled with I/O hub 1807 can include a local,internal, or embedded display device.

In at least one embodiment, processing subsystem 1801 includes one ormore parallel processor(s) 1812 coupled to memory hub 1805 via a bus orother communication link 1813. In at least one embodiment, communicationlink 1813 may use one of any number of standards based communicationlink technologies or protocols, such as, but not limited to PCI Express,or may be a vendor-specific communications interface or communicationsfabric. In at least one embodiment, one or more parallel processor(s)1812 form a computationally focused parallel or vector processing systemthat can include a large number of processing cores and/or processingclusters, such as a many-integrated core (MIC) processor. In at leastone embodiment, some or all of parallel processor(s) 1812 form agraphics processing subsystem that can output pixels to one of one ormore display device(s) 1810A coupled via I/O Hub 1807. In at least oneembodiment, parallel processor(s) 1812 can also include a displaycontroller and display interface (not shown) to enable a directconnection to one or more display device(s) 1810B.

In at least one embodiment, a system storage unit 1814 can connect toI/O hub 1807 to provide a storage mechanism for computing system 1800.In at least one embodiment, an I/O switch 1816 can be used to provide aninterface mechanism to enable connections between I/O hub 1807 and othercomponents, such as a network adapter 1818 and/or a wireless networkadapter 1819 that may be integrated into platform, and various otherdevices that can be added via one or more add-in device(s) 1820. In atleast one embodiment, network adapter 1818 can be an Ethernet adapter oranother wired network adapter. In at least one embodiment, wirelessnetwork adapter 1819 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

In at least one embodiment, computing system 1800 can include othercomponents not explicitly shown, including USB or other portconnections, optical storage drives, video capture devices, and like,may also be connected to I/O hub 1807. In at least one embodiment,communication paths interconnecting various components in FIG. 18 may beimplemented using any suitable protocols, such as PCI (PeripheralComponent Interconnect) based protocols (e.g., PCI-Express), or otherbus or point-to-point communication interfaces and/or protocol(s), suchas NV-Link high-speed interconnect, or interconnect protocols.

In at least one embodiment, parallel processor(s) 1812 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In at least one embodiment, parallel processor(s) 1812incorporate circuitry optimized for general purpose processing. In atleast embodiment, components of computing system 1800 may be integratedwith one or more other system elements on a single integrated circuit.For example, in at least one embodiment, parallel processor(s) 1812,memory hub 1805, processor(s) 1802, and I/O hub 1807 can be integratedinto a system on chip (SoC) integrated circuit. In at least oneembodiment, components of computing system 1800 can be integrated into asingle package to form a system in package (SIP) configuration. In atleast one embodiment, at least a portion of components of computingsystem 1800 can be integrated into a multi-chip module (MCM), which canbe interconnected with other multi-chip modules into a modular computingsystem.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in systemFIG. 1800 for inferencing or predicting operations based, at least inpart, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 18 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 18 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 18 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

Processors

FIG. 19A illustrates a parallel processor 1900 according to at least oneembodiment. In at least one embodiment, various components of parallelprocessor 1900 may be implemented using one or more integrated circuitdevices, such as programmable processors, application specificintegrated circuits (ASICs), or field programmable gate arrays (FPGA).In at least one embodiment, illustrated parallel processor 1900 is avariant of one or more parallel processor(s) 1812 shown in FIG. 18according to an exemplary embodiment.

In at least one embodiment, parallel processor 1900 includes a parallelprocessing unit 1902. In at least one embodiment, parallel processingunit 1902 includes an I/O unit 1904 that enables communication withother devices, including other instances of parallel processing unit1902. In at least one embodiment, I/O unit 1904 may be directlyconnected to other devices. In at least one embodiment, I/O unit 1904connects with other devices via use of a hub or switch interface, suchas a memory hub 1905. In at least one embodiment, connections betweenmemory hub 1905 and I/O unit 1904 form a communication link 1913. In atleast one embodiment, I/O unit 1904 connects with a host interface 1906and a memory crossbar 1916, where host interface 1906 receives commandsdirected to performing processing operations and memory crossbar 1916receives commands directed to performing memory operations.

In at least one embodiment, when host interface 1906 receives a commandbuffer via I/O unit 1904, host interface 1906 can direct work operationsto perform those commands to a front end 1908. In at least oneembodiment, front end 1908 couples with a scheduler 1910, which isconfigured to distribute commands or other work items to a processingcluster array 1912. In at least one embodiment, scheduler 1910 ensuresthat processing cluster array 1912 is properly configured and in a validstate before tasks are distributed to a cluster of processing clusterarray 1912. In at least one embodiment, scheduler 1910 is implementedvia firmware logic executing on a microcontroller. In at least oneembodiment, microcontroller implemented scheduler 1910 is configurableto perform complex scheduling and work distribution operations at coarseand fine granularity, enabling rapid preemption and context switching ofthreads executing on processing array 1912. In at least one embodiment,host software can prove workloads for scheduling on processing clusterarray 1912 via one of multiple graphics processing paths. In at leastone embodiment, workloads can then be automatically distributed acrossprocessing array cluster 1912 by scheduler 1910 logic within amicrocontroller including scheduler 1910.

In at least one embodiment, processing cluster array 1912 can include upto “N” processing clusters (e.g., cluster 1914A, cluster 1914B, throughcluster 1914N), where “N” represents a positive integer (which may be adifferent integer “N” than used in other figures). In at least oneembodiment, each cluster 1914A-1914N of processing cluster array 1912can execute a large number of concurrent threads. In at least oneembodiment, scheduler 1910 can allocate work to clusters 1914A-1914N ofprocessing cluster array 1912 using various scheduling and/or workdistribution algorithms, which may vary depending on workload arisingfor each type of program or computation. In at least one embodiment,scheduling can be handled dynamically by scheduler 1910, or can beassisted in part by compiler logic during compilation of program logicconfigured for execution by processing cluster array 1912. In at leastone embodiment, different clusters 1914A-1914N of processing clusterarray 1912 can be allocated for processing different types of programsor for performing different types of computations.

In at least one embodiment, processing cluster array 1912 can beconfigured to perform various types of parallel processing operations.In at least one embodiment, processing cluster array 1912 is configuredto perform general-purpose parallel compute operations. For example, inat least one embodiment, processing cluster array 1912 can include logicto execute processing tasks including filtering of video and/or audiodata, performing modeling operations, including physics operations, andperforming data transformations.

In at least one embodiment, processing cluster array 1912 is configuredto perform parallel graphics processing operations. In at least oneembodiment, processing cluster array 1912 can include additional logicto support execution of such graphics processing operations, includingbut not limited to, texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. In at least one embodiment, processing cluster array 1912 can beconfigured to execute graphics processing related shader programs suchas, but not limited to, vertex shaders, tessellation shaders, geometryshaders, and pixel shaders. In at least one embodiment, parallelprocessing unit 1902 can transfer data from system memory via I/O unit1904 for processing. In at least one embodiment, during processing,transferred data can be stored to on-chip memory (e.g., parallelprocessor memory 1922) during processing, then written back to systemmemory.

In at least one embodiment, when parallel processing unit 1902 is usedto perform graphics processing, scheduler 1910 can be configured todivide a processing workload into approximately equal sized tasks, tobetter enable distribution of graphics processing operations to multipleclusters 1914A-1914N of processing cluster array 1912. In at least oneembodiment, portions of processing cluster array 1912 can be configuredto perform different types of processing. For example, in at least oneembodiment, a first portion may be configured to perform vertex shadingand topology generation, a second portion may be configured to performtessellation and geometry shading, and a third portion may be configuredto perform pixel shading or other screen space operations, to produce arendered image for display. In at least one embodiment, intermediatedata produced by one or more of clusters 1914A-1914N may be stored inbuffers to allow intermediate data to be transmitted between clusters1914A-1914N for further processing.

In at least one embodiment, processing cluster array 1912 can receiveprocessing tasks to be executed via scheduler 1910, which receivescommands defining processing tasks from front end 1908. In at least oneembodiment, processing tasks can include indices of data to beprocessed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howdata is to be processed (e.g., what program is to be executed). In atleast one embodiment, scheduler 1910 may be configured to fetch indicescorresponding to tasks or may receive indices from front end 1908. In atleast one embodiment, front end 1908 can be configured to ensureprocessing cluster array 1912 is configured to a valid state before aworkload specified by incoming command buffers (e.g., batch-buffers,push buffers, etc.) is initiated.

In at least one embodiment, each of one or more instances of parallelprocessing unit 1902 can couple with a parallel processor memory 1922.In at least one embodiment, parallel processor memory 1922 can beaccessed via memory crossbar 1916, which can receive memory requestsfrom processing cluster array 1912 as well as I/O unit 1904. In at leastone embodiment, memory crossbar 1916 can access parallel processormemory 1922 via a memory interface 1918. In at least one embodiment,memory interface 1918 can include multiple partition units (e.g.,partition unit 1920A, partition unit 1920B, through partition unit1920N) that can each couple to a portion (e.g., memory unit) of parallelprocessor memory 1922. In at least one embodiment, a number of partitionunits 1920A-1920N is configured to be equal to a number of memory units,such that a first partition unit 1920A has a corresponding first memoryunit 1924A, a second partition unit 1920B has a corresponding memoryunit 1924B, and an N-th partition unit 1920N has a corresponding N-thmemory unit 1924N. In at least one embodiment, a number of partitionunits 1920A-1920N may not be equal to a number of memory units.

In at least one embodiment, memory units 1924A-1924N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In at least one embodiment, memory units 1924A-1924N may alsoinclude 3D stacked memory, including but not limited to high bandwidthmemory (HBM). In at least one embodiment, render targets, such as framebuffers or texture maps may be stored across memory units 1924A-1924N,allowing partition units 1920A-1920N to write portions of each rendertarget in parallel to efficiently use available bandwidth of parallelprocessor memory 1922. In at least one embodiment, a local instance ofparallel processor memory 1922 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In at least one embodiment, any one of clusters 1914A-1914N ofprocessing cluster array 1912 can process data that will be written toany of memory units 1924A-1924N within parallel processor memory 1922.In at least one embodiment, memory crossbar 1916 can be configured totransfer an output of each cluster 1914A-1914N to any partition unit1920A-1920N or to another cluster 1914A-1914N, which can performadditional processing operations on an output. In at least oneembodiment, each cluster 1914A-1914N can communicate with memoryinterface 1918 through memory crossbar 1916 to read from or write tovarious external memory devices. In at least one embodiment, memorycrossbar 1916 has a connection to memory interface 1918 to communicatewith I/O unit 1904, as well as a connection to a local instance ofparallel processor memory 1922, enabling processing units withindifferent processing clusters 1914A-1914N to communicate with systemmemory or other memory that is not local to parallel processing unit1902. In at least one embodiment, memory crossbar 1916 can use virtualchannels to separate traffic streams between clusters 1914A-1914N andpartition units 1920A-1920N.

In at least one embodiment, multiple instances of parallel processingunit 1902 can be provided on a single add-in card, or multiple add-incards can be interconnected. In at least one embodiment, differentinstances of parallel processing unit 1902 can be configured tointeroperate even if different instances have different numbers ofprocessing cores, different amounts of local parallel processor memory,and/or other configuration differences. For example, in at least oneembodiment, some instances of parallel processing unit 1902 can includehigher precision floating point units relative to other instances. In atleast one embodiment, systems incorporating one or more instances ofparallel processing unit 1902 or parallel processor 1900 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 19B is a block diagram of a partition unit 1920 according to atleast one embodiment. In at least one embodiment, partition unit 1920 isan instance of one of partition units 1920A-1920N of FIG. 19A. In atleast one embodiment, partition unit 1920 includes an L2 cache 1921, aframe buffer interface 1925, and a ROP 1926 (raster operations unit). Inat least one embodiment, L2 cache 1921 is a read/write cache that isconfigured to perform load and store operations received from memorycrossbar 1916 and ROP 1926. In at least one embodiment, read misses andurgent write-back requests are output by L2 cache 1921 to frame bufferinterface 1925 for processing. In at least one embodiment, updates canalso be sent to a frame buffer via frame buffer interface 1925 forprocessing. In at least one embodiment, frame buffer interface 1925interfaces with one of memory units in parallel processor memory, suchas memory units 1924A-1924N of FIG. 19 (e.g., within parallel processormemory 1922).

In at least one embodiment, ROP 1926 is a processing unit that performsraster operations such as stencil, z test, blending, etc. In at leastone embodiment, ROP 1926 then outputs processed graphics data that isstored in graphics memory. In at least one embodiment, ROP 1926 includescompression logic to compress depth or color data that is written tomemory and decompress depth or color data that is read from memory. Inat least one embodiment, compression logic can be lossless compressionlogic that makes use of one or more of multiple compression algorithms.In at least one embodiment, a type of compression that is performed byROP 1926 can vary based on statistical characteristics of data to becompressed. For example, in at least one embodiment, delta colorcompression is performed on depth and color data on a per-tile basis.

In at least one embodiment, ROP 1926 is included within each processingcluster (e.g., cluster 1914A-1914N of FIG. 19A) instead of withinpartition unit 1920. In at least one embodiment, read and write requestsfor pixel data are transmitted over memory crossbar 1916 instead ofpixel fragment data. In at least one embodiment, processed graphics datamay be displayed on a display device, such as one of one or more displaydevice(s) 1810 of FIG. 18, routed for further processing by processor(s)1802, or routed for further processing by one of processing entitieswithin parallel processor 1900 of FIG. 19A.

FIG. 19C is a block diagram of a processing cluster 1914 within aparallel processing unit according to at least one embodiment. In atleast one embodiment, a processing cluster is an instance of one ofprocessing clusters 1914A-1914N of FIG. 19A. In at least one embodiment,processing cluster 1914 can be configured to execute many threads inparallel, where “thread” refers to an instance of a particular programexecuting on a particular set of input data. In at least one embodiment,single-instruction, multiple-data (SIMD) instruction issue techniquesare used to support parallel execution of a large number of threadswithout providing multiple independent instruction units. In at leastone embodiment, single-instruction, multiple-thread (SIMT) techniquesare used to support parallel execution of a large number of generallysynchronized threads, using a common instruction unit configured toissue instructions to a set of processing engines within each one ofprocessing clusters.

In at least one embodiment, operation of processing cluster 1914 can becontrolled via a pipeline manager 1932 that distributes processing tasksto SIMT parallel processors. In at least one embodiment, pipelinemanager 1932 receives instructions from scheduler 1910 of FIG. 19A andmanages execution of those instructions via a graphics multiprocessor1934 and/or a texture unit 1936. In at least one embodiment, graphicsmultiprocessor 1934 is an exemplary instance of a SIMT parallelprocessor. However, in at least one embodiment, various types of SIMTparallel processors of differing architectures may be included withinprocessing cluster 1914. In at least one embodiment, one or moreinstances of graphics multiprocessor 1934 can be included within aprocessing cluster 1914. In at least one embodiment, graphicsmultiprocessor 1934 can process data and a data crossbar 1940 can beused to distribute processed data to one of multiple possibledestinations, including other shader units. In at least one embodiment,pipeline manager 1932 can facilitate distribution of processed data byspecifying destinations for processed data to be distributed via datacrossbar 1940.

In at least one embodiment, each graphics multiprocessor 1934 withinprocessing cluster 1914 can include an identical set of functionalexecution logic (e.g., arithmetic logic units, load-store units, etc.).In at least one embodiment, functional execution logic can be configuredin a pipelined manner in which new instructions can be issued beforeprevious instructions are complete. In at least one embodiment,functional execution logic supports a variety of operations includinginteger and floating point arithmetic, comparison operations, Booleanoperations, bit-shifting, and computation of various algebraicfunctions. In at least one embodiment, same functional-unit hardware canbe leveraged to perform different operations and any combination offunctional units may be present.

In at least one embodiment, instructions transmitted to processingcluster 1914 constitute a thread. In at least one embodiment, a set ofthreads executing across a set of parallel processing engines is athread group. In at least one embodiment, a thread group executes acommon program on different input data. In at least one embodiment, eachthread within a thread group can be assigned to a different processingengine within a graphics multiprocessor 1934. In at least oneembodiment, a thread group may include fewer threads than a number ofprocessing engines within graphics multiprocessor 1934. In at least oneembodiment, when a thread group includes fewer threads than a number ofprocessing engines, one or more of processing engines may be idle duringcycles in which that thread group is being processed. In at least oneembodiment, a thread group may also include more threads than a numberof processing engines within graphics multiprocessor 1934. In at leastone embodiment, when a thread group includes more threads than number ofprocessing engines within graphics multiprocessor 1934, processing canbe performed over consecutive clock cycles. In at least one embodiment,multiple thread groups can be executed concurrently on a graphicsmultiprocessor 1934.

In at least one embodiment, graphics multiprocessor 1934 includes aninternal cache memory to perform load and store operations. In at leastone embodiment, graphics multiprocessor 1934 can forego an internalcache and use a cache memory (e.g., L1 cache 1948) within processingcluster 1914. In at least one embodiment, each graphics multiprocessor1934 also has access to L2 caches within partition units (e.g.,partition units 1920A-1920N of FIG. 19A) that are shared among allprocessing clusters 1914 and may be used to transfer data betweenthreads. In at least one embodiment, graphics multiprocessor 1934 mayalso access off-chip global memory, which can include one or more oflocal parallel processor memory and/or system memory. In at least oneembodiment, any memory external to parallel processing unit 1902 may beused as global memory. In at least one embodiment, processing cluster1914 includes multiple instances of graphics multiprocessor 1934 and canshare common instructions and data, which may be stored in L1 cache1948.

In at least one embodiment, each processing cluster 1914 may include anMMU 1945 (memory management unit) that is configured to map virtualaddresses into physical addresses. In at least one embodiment, one ormore instances of MMU 1945 may reside within memory interface 1918 ofFIG. 19A. In at least one embodiment, MMU 1945 includes a set of pagetable entries (PTEs) used to map a virtual address to a physical addressof a tile and optionally a cache line index. In at least one embodiment,MMU 1945 may include address translation lookaside buffers (TLB) orcaches that may reside within graphics multiprocessor 1934 or L1 1948cache or processing cluster 1914. In at least one embodiment, a physicaladdress is processed to distribute surface data access locally to allowfor efficient request interleaving among partition units. In at leastone embodiment, a cache line index may be used to determine whether arequest for a cache line is a hit or miss.

In at least one embodiment, a processing cluster 1914 may be configuredsuch that each graphics multiprocessor 1934 is coupled to a texture unit1936 for performing texture mapping operations, e.g., determiningtexture sample positions, reading texture data, and filtering texturedata. In at least one embodiment, texture data is read from an internaltexture L1 cache (not shown) or from an L1 cache within graphicsmultiprocessor 1934 and is fetched from an L2 cache, local parallelprocessor memory, or system memory, as needed. In at least oneembodiment, each graphics multiprocessor 1934 outputs processed tasks todata crossbar 1940 to provide processed task to another processingcluster 1914 for further processing or to store processed task in an L2cache, local parallel processor memory, or system memory via memorycrossbar 1916. In at least one embodiment, a preROP 1942 (pre-rasteroperations unit) is configured to receive data from graphicsmultiprocessor 1934, and direct data to ROP units, which may be locatedwith partition units as described herein (e.g., partition units1920A-1920N of FIG. 19A). In at least one embodiment, preROP 1942 unitcan perform optimizations for color blending, organizing pixel colordata, and performing address translations.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in graphicsprocessing cluster 1914 for inferencing or predicting operations based,at least in part, on weight parameters calculated using neural networktraining operations, neural network functions and/or architectures, orneural network use cases described herein.

FIG. 19D shows a graphics multiprocessor 1934 according to at least oneembodiment. In at least one embodiment, graphics multiprocessor 1934couples with pipeline manager 1932 of processing cluster 1914. In atleast one embodiment, graphics multiprocessor 1934 has an executionpipeline including but not limited to an instruction cache 1952, aninstruction unit 1954, an address mapping unit 1956, a register file1958, one or more general purpose graphics processing unit (GPGPU) cores1962, and one or more load/store units 1966. In at least one embodiment,GPGPU cores 1962 and load/store units 1966 are coupled with cache memory1972 and shared memory 1970 via a memory and cache interconnect 1968.

In at least one embodiment, instruction cache 1952 receives a stream ofinstructions to execute from pipeline manager 1932. In at least oneembodiment, instructions are cached in instruction cache 1952 anddispatched for execution by an instruction unit 1954. In at least oneembodiment, instruction unit 1954 can dispatch instructions as threadgroups (e.g., warps), with each thread of thread group assigned to adifferent execution unit within GPGPU cores 1962. In at least oneembodiment, an instruction can access any of a local, shared, or globaladdress space by specifying an address within a unified address space.In at least one embodiment, address mapping unit 1956 can be used totranslate addresses in a unified address space into a distinct memoryaddress that can be accessed by load/store units 1966.

In at least one embodiment, register file 1958 provides a set ofregisters for functional units of graphics multiprocessor 1934. In atleast one embodiment, register file 1958 provides temporary storage foroperands connected to data paths of functional units (e.g., GPGPU cores1962, load/store units 1966) of graphics multiprocessor 1934. In atleast one embodiment, register file 1958 is divided between each offunctional units such that each functional unit is allocated a dedicatedportion of register file 1958. In at least one embodiment, register file1958 is divided between different warps being executed by graphicsmultiprocessor 1934.

In at least one embodiment, GPGPU cores 1962 can each include floatingpoint units (FPUs) and/or integer arithmetic logic units (ALUs) that areused to execute instructions of graphics multiprocessor 1934. In atleast one embodiment, GPGPU cores 1962 can be similar in architecture orcan differ in architecture. In at least one embodiment, a first portionof GPGPU cores 1962 include a single precision FPU and an integer ALUwhile a second portion of GPGPU cores include a double precision FPU. Inat least one embodiment, FPUs can implement IEEE 754-2008 standardfloating point arithmetic or enable variable precision floating pointarithmetic. In at least one embodiment, graphics multiprocessor 1934 canadditionally include one or more fixed function or special functionunits to perform specific functions such as copy rectangle or pixelblending operations. In at least one embodiment, one or more of GPGPUcores 1962 can also include fixed or special function logic.

In at least one embodiment, GPGPU cores 1962 include SIMD logic capableof performing a single instruction on multiple sets of data. In at leastone embodiment, GPGPU cores 1962 can physically execute SIMD4, SIMD8,and SIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. In at least one embodiment, SIMD instructions for GPGPUcores can be generated at compile time by a shader compiler orautomatically generated when executing programs written and compiled forsingle program multiple data (SPMD) or SIMT architectures. In at leastone embodiment, multiple threads of a program configured for an SIMTexecution model can executed via a single SIMD instruction. For example,in at least one embodiment, eight SIMT threads that perform same orsimilar operations can be executed in parallel via a single SIMD8 logicunit.

In at least one embodiment, memory and cache interconnect 1968 is aninterconnect network that connects each functional unit of graphicsmultiprocessor 1934 to register file 1958 and to shared memory 1970. Inat least one embodiment, memory and cache interconnect 1968 is acrossbar interconnect that allows load/store unit 1966 to implement loadand store operations between shared memory 1970 and register file 1958.In at least one embodiment, register file 1958 can operate at a samefrequency as GPGPU cores 1962, thus data transfer between GPGPU cores1962 and register file 1958 can have very low latency. In at least oneembodiment, shared memory 1970 can be used to enable communicationbetween threads that execute on functional units within graphicsmultiprocessor 1934. In at least one embodiment, cache memory 1972 canbe used as a data cache for example, to cache texture data communicatedbetween functional units and texture unit 1936. In at least oneembodiment, shared memory 1970 can also be used as a program managedcache. In at least one embodiment, threads executing on GPGPU cores 1962can programmatically store data within shared memory in addition toautomatically cached data that is stored within cache memory 1972.

In at least one embodiment, a parallel processor or GPGPU as describedherein is communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. In atleast one embodiment, a GPU may be communicatively coupled to hostprocessor/cores over a bus or other interconnect (e.g., a high-speedinterconnect such as PCIe or NVLink). In at least one embodiment, a GPUmay be integrated on a package or chip as cores and communicativelycoupled to cores over an internal processor bus/interconnect internal toa package or chip. In at least one embodiment, regardless a manner inwhich a GPU is connected, processor cores may allocate work to such GPUin a form of sequences of commands/instructions contained in a workdescriptor. In at least one embodiment, that GPU then uses dedicatedcircuitry/logic for efficiently processing these commands/instructions.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in graphicsmultiprocessor 1934 for inferencing or predicting operations based, atleast in part, on weight parameters calculated using neural networktraining operations, neural network functions and/or architectures, orneural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG.19A-FIG. 19D are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 19A-FIG. 19D are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.19A-FIG. 19D are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIG. 20 illustrates a multi-GPU computing system 2000, according to atleast one embodiment. In at least one embodiment, multi-GPU computingsystem 2000 can include a processor 2002 coupled to multiple generalpurpose graphics processing units (GPGPUs) 2006A-D via a host interfaceswitch 2004. In at least one embodiment, host interface switch 2004 is aPCI express switch device that couples processor 2002 to a PCI expressbus over which processor 2002 can communicate with GPGPUs 2006A-D. In atleast one embodiment, GPGPUs 2006A-D can interconnect via a set ofhigh-speed point-to-point GPU-to-GPU links 2016. In at least oneembodiment, GPU-to-GPU links 2016 connect to each of GPGPUs 2006A-D viaa dedicated GPU link. In at least one embodiment, P2P GPU links 2016enable direct communication between each of GPGPUs 2006A-D withoutrequiring communication over host interface bus 2004 to which processor2002 is connected. In at least one embodiment, with GPU-to-GPU trafficdirected to P2P GPU links 2016, host interface bus 2004 remainsavailable for system memory access or to communicate with otherinstances of multi-GPU computing system 2000, for example, via one ormore network devices. While in at least one embodiment GPGPUs 2006A-Dconnect to processor 2002 via host interface switch 2004, in at leastone embodiment processor 2002 includes direct support for P2P GPU links2016 and can connect directly to GPGPUs 2006A-D.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in multi-GPUcomputing system 2000 for inferencing or predicting operations based, atleast in part, on weight parameters calculated using neural networktraining operations, neural network functions and/or architectures, orneural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 20 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 20 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 20 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 21 is a block diagram of a graphics processor 2100, according to atleast one embodiment. In at least one embodiment, graphics processor2100 includes a ring interconnect 2102, a pipeline front-end 2104, amedia engine 2137, and graphics cores 2180A-2180N. In at least oneembodiment, ring interconnect 2102 couples graphics processor 2100 toother processing units, including other graphics processors or one ormore general-purpose processor cores. In at least one embodiment,graphics processor 2100 is one of many processors integrated within amulti-core processing system.

In at least one embodiment, graphics processor 2100 receives batches ofcommands via ring interconnect 2102. In at least one embodiment,incoming commands are interpreted by a command streamer 2103 in pipelinefront-end 2104. In at least one embodiment, graphics processor 2100includes scalable execution logic to perform 3D geometry processing andmedia processing via graphics core(s) 2180A-2180N. In at least oneembodiment, for 3D geometry processing commands, command streamer 2103supplies commands to geometry pipeline 2136. In at least one embodiment,for at least some media processing commands, command streamer 2103supplies commands to a video front end 2134, which couples with mediaengine 2137. In at least one embodiment, media engine 2137 includes aVideo Quality Engine (VQE) 2130 for video and image post-processing anda multi-format encode/decode (MFX) 2133 engine to providehardware-accelerated media data encoding and decoding. In at least oneembodiment, geometry pipeline 2136 and media engine 2137 each generateexecution threads for thread execution resources provided by at leastone graphics core 2180.

In at least one embodiment, graphics processor 2100 includes scalablethread execution resources featuring graphics cores 2180A-2180N (whichcan be modular and are sometimes referred to as core slices), eachhaving multiple sub-cores 2150A-50N, 2160A-2160N (sometimes referred toas core sub-slices). In at least one embodiment, graphics processor 2100can have any number of graphics cores 2180A. In at least one embodiment,graphics processor 2100 includes a graphics core 2180A having at least afirst sub-core 2150A and a second sub-core 2160A. In at least oneembodiment, graphics processor 2100 is a low power processor with asingle sub-core (e.g., 2150A). In at least one embodiment, graphicsprocessor 2100 includes multiple graphics cores 2180A-2180N, eachincluding a set of first sub-cores 2150A-2150N and a set of secondsub-cores 2160A-2160N. In at least one embodiment, each sub-core infirst sub-cores 2150A-2150N includes at least a first set of executionunits 2152A-2152N and media/texture samplers 2154A-2154N. In at leastone embodiment, each sub-core in second sub-cores 2160A-2160N includesat least a second set of execution units 2162A-2162N and samplers2164A-2164N. In at least one embodiment, each sub-core 2150A-2150N,2160A-2160N shares a set of shared resources 2170A-2170N. In at leastone embodiment, shared resources include shared cache memory and pixeloperation logic.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, inference and/or training logic 615 may be used in graphicsprocessor 2100 for inferencing or predicting operations based, at leastin part, on weight parameters calculated using neural network trainingoperations, neural network functions and/or architectures, or neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 21 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 21 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 21 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 22 is a block diagram illustrating micro-architecture for aprocessor 2200 that may include logic circuits to perform instructions,according to at least one embodiment. In at least one embodiment,processor 2200 may perform instructions, including x86 instructions, ARMinstructions, specialized instructions for application-specificintegrated circuits (ASICs), etc. In at least one embodiment, processor2200 may include registers to store packed data, such as 64-bit wideMMX™ registers in microprocessors enabled with MMX technology from IntelCorporation of Santa Clara, Calif. In at least one embodiment, MMXregisters, available in both integer and floating point forms, mayoperate with packed data elements that accompany single instruction,multiple data (“SIMD”) and streaming SIMD extensions (“SSE”)instructions. In at least one embodiment, 128-bit wide XMM registersrelating to SSE2, SSE3, SSE4, AVX, or beyond (referred to generically as“SSEx”) technology may hold such packed data operands. In at least oneembodiment, processor 2200 may perform instructions to acceleratemachine learning or deep learning algorithms, training, or inferencing.

In at least one embodiment, processor 2200 includes an in-order frontend (“front end”) 2201 to fetch instructions to be executed and prepareinstructions to be used later in a processor pipeline. In at least oneembodiment, front end 2201 may include several units. In at least oneembodiment, an instruction prefetcher 2226 fetches instructions frommemory and feeds instructions to an instruction decoder 2228 which inturn decodes or interprets instructions. For example, in at least oneembodiment, instruction decoder 2228 decodes a received instruction intoone or more operations called “micro-instructions” or “micro-operations”(also called “micro ops” or “uops”) that a machine may execute. In atleast one embodiment, instruction decoder 2228 parses an instructioninto an opcode and corresponding data and control fields that may beused by micro-architecture to perform operations in accordance with atleast one embodiment. In at least one embodiment, a trace cache 2230 mayassemble decoded uops into program ordered sequences or traces in a uopqueue 2234 for execution. In at least one embodiment, when trace cache2230 encounters a complex instruction, a microcode ROM 2232 providesuops needed to complete an operation.

In at least one embodiment, some instructions may be converted into asingle micro-op, whereas others need several micro-ops to complete fulloperation. In at least one embodiment, if more than four micro-ops areneeded to complete an instruction, instruction decoder 2228 may accessmicrocode ROM 2232 to perform that instruction. In at least oneembodiment, an instruction may be decoded into a small number ofmicro-ops for processing at instruction decoder 2228. In at least oneembodiment, an instruction may be stored within microcode ROM 2232should a number of micro-ops be needed to accomplish such operation. Inat least one embodiment, trace cache 2230 refers to an entry pointprogrammable logic array (“PLA”) to determine a correctmicro-instruction pointer for reading microcode sequences to completeone or more instructions from microcode ROM 2232 in accordance with atleast one embodiment. In at least one embodiment, after microcode ROM2232 finishes sequencing micro-ops for an instruction, front end 2201 ofa machine may resume fetching micro-ops from trace cache 2230.

In at least one embodiment, out-of-order execution engine (“out of orderengine”) 2203 may prepare instructions for execution. In at least oneembodiment, out-of-order execution logic has a number of buffers tosmooth out and re-order flow of instructions to optimize performance asthey go down a pipeline and get scheduled for execution. In at least oneembodiment, out-of-order execution engine 2203 includes, withoutlimitation, an allocator/register renamer 2240, a memory uop queue 2242,an integer/floating point uop queue 2244, a memory scheduler 2246, afast scheduler 2202, a slow/general floating point scheduler(“slow/general FP scheduler”) 2204, and a simple floating pointscheduler (“simple FP scheduler”) 2206. In at least one embodiment, fastschedule 2202, slow/general floating point scheduler 2204, and simplefloating point scheduler 2206 are also collectively referred to hereinas “uop schedulers 2202, 2204, 2206.” In at least one embodiment,allocator/register renamer 2240 allocates machine buffers and resourcesthat each uop needs in order to execute. In at least one embodiment,allocator/register renamer 2240 renames logic registers onto entries ina register file. In at least one embodiment, allocator/register renamer2240 also allocates an entry for each uop in one of two uop queues,memory uop queue 2242 for memory operations and integer/floating pointuop queue 2244 for non-memory operations, in front of memory scheduler2246 and uop schedulers 2202, 2204, 2206. In at least one embodiment,uop schedulers 2202, 2204, 2206, determine when a uop is ready toexecute based on readiness of their dependent input register operandsources and availability of execution resources uops need to completetheir operation. In at least one embodiment, fast scheduler 2202 mayschedule on each half of a main clock cycle while slow/general floatingpoint scheduler 2204 and simple floating point scheduler 2206 mayschedule once per main processor clock cycle. In at least oneembodiment, uop schedulers 2202, 2204, 2206 arbitrate for dispatch portsto schedule uops for execution.

In at least one embodiment, execution block 2211 includes, withoutlimitation, an integer register file/bypass network 2208, a floatingpoint register file/bypass network (“FP register file/bypass network”)2210, address generation units (“AGUs”) 2212 and 2214, fast ArithmeticLogic Units (ALUs) (“fast ALUs”) 2216 and 2218, a slow Arithmetic LogicUnit (“slow ALU”) 2220, a floating point ALU (“FP”) 2222, and a floatingpoint move unit (“FP move”) 2224. In at least one embodiment, integerregister file/bypass network 2208 and floating point registerfile/bypass network 2210 are also referred to herein as “register files2208, 2210.” In at least one embodiment, AGUSs 2212 and 2214, fast ALUs2216 and 2218, slow ALU 2220, floating point ALU 2222, and floatingpoint move unit 2224 are also referred to herein as “execution units2212, 2214, 2216, 2218, 2220, 2222, and 2224.” In at least oneembodiment, execution block 2211 may include, without limitation, anynumber (including zero) and type of register files, bypass networks,address generation units, and execution units, in any combination.

In at least one embodiment, register networks 2208, 2210 may be arrangedbetween uop schedulers 2202, 2204, 2206, and execution units 2212, 2214,2216, 2218, 2220, 2222, and 2224. In at least one embodiment, integerregister file/bypass network 2208 performs integer operations. In atleast one embodiment, floating point register file/bypass network 2210performs floating point operations. In at least one embodiment, each ofregister networks 2208, 2210 may include, without limitation, a bypassnetwork that may bypass or forward just completed results that have notyet been written into a register file to new dependent uops. In at leastone embodiment, register networks 2208, 2210 may communicate data witheach other. In at least one embodiment, integer register file/bypassnetwork 2208 may include, without limitation, two separate registerfiles, one register file for a low-order thirty-two bits of data and asecond register file for a high order thirty-two bits of data. In atleast one embodiment, floating point register file/bypass network 2210may include, without limitation, 128-bit wide entries because floatingpoint instructions typically have operands from 64 to 128 bits in width.

In at least one embodiment, execution units 2212, 2214, 2216, 2218,2220, 2222, 2224 may execute instructions. In at least one embodiment,register networks 2208, 2210 store integer and floating point dataoperand values that micro-instructions need to execute. In at least oneembodiment, processor 2200 may include, without limitation, any numberand combination of execution units 2212, 2214, 2216, 2218, 2220, 2222,2224. In at least one embodiment, floating point ALU 2222 and floatingpoint move unit 2224, may execute floating point, MMX, SIMD, AVX andSSE, or other operations, including specialized machine learninginstructions. In at least one embodiment, floating point ALU 2222 mayinclude, without limitation, a 64-bit by 64-bit floating point dividerto execute divide, square root, and remainder micro ops. In at least oneembodiment, instructions involving a floating point value may be handledwith floating point hardware. In at least one embodiment, ALU operationsmay be passed to fast ALUs 2216, 2218. In at least one embodiment, fastALUS 2216, 2218 may execute fast operations with an effective latency ofhalf a clock cycle. In at least one embodiment, most complex integeroperations go to slow ALU 2220 as slow ALU 2220 may include, withoutlimitation, integer execution hardware for long-latency type ofoperations, such as a multiplier, shifts, flag logic, and branchprocessing. In at least one embodiment, memory load/store operations maybe executed by AGUs 2212, 2214. In at least one embodiment, fast ALU2216, fast ALU 2218, and slow ALU 2220 may perform integer operations on64-bit data operands. In at least one embodiment, fast ALU 2216, fastALU 2218, and slow ALU 2220 may be implemented to support a variety ofdata bit sizes including sixteen, thirty-two, 128, 256, etc. In at leastone embodiment, floating point ALU 2222 and floating point move unit2224 may be implemented to support a range of operands having bits ofvarious widths, such as 128-bit wide packed data operands in conjunctionwith SIMD and multimedia instructions.

In at least one embodiment, uop schedulers 2202, 2204, 2206 dispatchdependent operations before a parent load has finished executing. In atleast one embodiment, as uops may be speculatively scheduled andexecuted in processor 2200, processor 2200 may also include logic tohandle memory misses. In at least one embodiment, if a data load missesin a data cache, there may be dependent operations in flight in apipeline that have left a scheduler with temporarily incorrect data. Inat least one embodiment, a replay mechanism tracks and re-executesinstructions that use incorrect data. In at least one embodiment,dependent operations might need to be replayed and independent ones maybe allowed to complete. In at least one embodiment, schedulers and areplay mechanism of at least one embodiment of a processor may also bedesigned to catch instruction sequences for text string comparisonoperations.

In at least one embodiment, “registers” may refer to on-board processorstorage locations that may be used as part of instructions to identifyoperands. In at least one embodiment, registers may be those that may beusable from outside of a processor (from a programmer's perspective). Inat least one embodiment, registers might not be limited to a particulartype of circuit. Rather, in at least one embodiment, a register maystore data, provide data, and perform functions described herein. In atleast one embodiment, registers described herein may be implemented bycircuitry within a processor using any number of different techniques,such as dedicated physical registers, dynamically allocated physicalregisters using register renaming, combinations of dedicated anddynamically allocated physical registers, etc. In at least oneembodiment, integer registers store 32-bit integer data. A register fileof at least one embodiment also contains eight multimedia SIMD registersfor packed data.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment portions or all of inference and/or training logic 615 may beincorporated into execution block 2211 and other memory or registersshown or not shown. For example, in at least one embodiment, trainingand/or inferencing techniques described herein may use one or more ofALUs illustrated in execution block 2211. Moreover, weight parametersmay be stored in on-chip or off-chip memory and/or registers (shown ornot shown) that configure ALUs of execution block 2211 to perform one ormore machine learning algorithms, neural network architectures, usecases, or training techniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 22 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 22 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 22 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 23 illustrates a deep learning application processor 2300,according to at least one embodiment. In at least one embodiment, deeplearning application processor 2300 uses instructions that, if executedby deep learning application processor 2300, cause deep learningapplication processor 2300 to perform some or all of processes andtechniques described throughout this disclosure. In at least oneembodiment, deep learning application processor 2300 is anapplication-specific integrated circuit (ASIC). In at least oneembodiment, application processor 2300 performs matrix multiplyoperations either “hard-wired” into hardware as a result of performingone or more instructions or both. In at least one embodiment, deeplearning application processor 2300 includes, without limitation,processing clusters 2310(1)-2310(12), Inter-Chip Links (“ICLs”)2320(1)-2320(12), Inter-Chip Controllers (“ICCs”) 2330(1)-2330(2),high-bandwidth memory second generation (“HBM2”) 2340(1)-2340(4), memorycontrollers (“Mem Ctrlrs”) 2342(1)-2342(4), high bandwidth memoryphysical layer (“HBM PHY”) 2344(1)-2344(4), a management-controllercentral processing unit (“management-controller CPU”) 2350, a SerialPeripheral Interface, Inter-Integrated Circuit, and General PurposeInput/Output block (“SPI, I²C, GPIO”) 2360, a peripheral componentinterconnect express controller and direct memory access block (“PCIeController and DMA”) 2370, and a sixteen-lane peripheral componentinterconnect express port (“PCI Express×16”) 2380.

In at least one embodiment, processing clusters 2310 may perform deeplearning operations, including inference or prediction operations basedon weight parameters calculated one or more training techniques,including those described herein. In at least one embodiment, eachprocessing cluster 2310 may include, without limitation, any number andtype of processors. In at least one embodiment, deep learningapplication processor 2300 may include any number and type of processingclusters 2300. In at least one embodiment, Inter-Chip Links 2320 arebi-directional. In at least one embodiment, Inter-Chip Links 2320 andInter-Chip Controllers 2330 enable multiple deep learning applicationprocessors 2300 to exchange information, including activationinformation resulting from performing one or more machine learningalgorithms embodied in one or more neural networks. In at least oneembodiment, deep learning application processor 2300 may include anynumber (including zero) and type of ICLs 2320 and ICCs 2330.

In at least one embodiment, HBM2s 2340 provide a total of 32 Gigabytes(GB) of memory. In at least one embodiment, HBM2 2340(i) is associatedwith both memory controller 2342(i) and HBM PHY 2344(i) where “i” is anarbitrary integer. In at least one embodiment, any number of HBM2s 2340may provide any type and total amount of high bandwidth memory and maybe associated with any number (including zero) and type of memorycontrollers 2342 and HBM PHYs 2344. In at least one embodiment, SPI,I²C, GPIO 2360, PCIe Controller and DMA 2370, and/or PCIe 2380 may bereplaced with any number and type of blocks that enable any number andtype of communication standards in any technically feasible fashion.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, deep learning application processor is used to train amachine learning model, such as a neural network, to predict or inferinformation provided to deep learning application processor 2300. In atleast one embodiment, deep learning application processor 2300 is usedto infer or predict information based on a trained machine learningmodel (e.g., neural network) that has been trained by another processoror system or by deep learning application processor 2300. In at leastone embodiment, processor 2300 may be used to perform one or more neuralnetwork use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 23 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 23 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 23 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 24 is a block diagram of a neuromorphic processor 2400, accordingto at least one embodiment. In at least one embodiment, neuromorphicprocessor 2400 may receive one or more inputs from sources external toneuromorphic processor 2400. In at least one embodiment, these inputsmay be transmitted to one or more neurons 2402 within neuromorphicprocessor 2400. In at least one embodiment, neurons 2402 and componentsthereof may be implemented using circuitry or logic, including one ormore arithmetic logic units (ALUs). In at least one embodiment,neuromorphic processor 2400 may include, without limitation, thousandsor millions of instances of neurons 2402, but any suitable number ofneurons 2402 may be used. In at least one embodiment, each instance ofneuron 2402 may include a neuron input 2404 and a neuron output 2406. Inat least one embodiment, neurons 2402 may generate outputs that may betransmitted to inputs of other instances of neurons 2402. For example,in at least one embodiment, neuron inputs 2404 and neuron outputs 2406may be interconnected via synapses 2408.

In at least one embodiment, neurons 2402 and synapses 2408 may beinterconnected such that neuromorphic processor 2400 operates to processor analyze information received by neuromorphic processor 2400. In atleast one embodiment, neurons 2402 may transmit an output pulse (or“fire” or “spike”) when inputs received through neuron input 2404 exceeda threshold. In at least one embodiment, neurons 2402 may sum orintegrate signals received at neuron inputs 2404. For example, in atleast one embodiment, neurons 2402 may be implemented as leakyintegrate-and-fire neurons, wherein if a sum (referred to as a “membranepotential”) exceeds a threshold value, neuron 2402 may generate anoutput (or “fire”) using a transfer function such as a sigmoid orthreshold function. In at least one embodiment, a leakyintegrate-and-fire neuron may sum signals received at neuron inputs 2404into a membrane potential and may also apply a decay factor (or leak) toreduce a membrane potential. In at least one embodiment, a leakyintegrate-and-fire neuron may fire if multiple input signals arereceived at neuron inputs 2404 rapidly enough to exceed a thresholdvalue (i.e., before a membrane potential decays too low to fire). In atleast one embodiment, neurons 2402 may be implemented using circuits orlogic that receive inputs, integrate inputs into a membrane potential,and decay a membrane potential. In at least one embodiment, inputs maybe averaged, or any other suitable transfer function may be used.Furthermore, in at least one embodiment, neurons 2402 may include,without limitation, comparator circuits or logic that generate an outputspike at neuron output 2406 when result of applying a transfer functionto neuron input 2404 exceeds a threshold. In at least one embodiment,once neuron 2402 fires, it may disregard previously received inputinformation by, for example, resetting a membrane potential to 0 oranother suitable default value. In at least one embodiment, oncemembrane potential is reset to 0, neuron 2402 may resume normaloperation after a suitable period of time (or refractory period).

In at least one embodiment, neurons 2402 may be interconnected throughsynapses 2408. In at least one embodiment, synapses 2408 may operate totransmit signals from an output of a first neuron 2402 to an input of asecond neuron 2402. In at least one embodiment, neurons 2402 maytransmit information over more than one instance of synapse 2408. In atleast one embodiment, one or more instances of neuron output 2406 may beconnected, via an instance of synapse 2408, to an instance of neuroninput 2404 in same neuron 2402. In at least one embodiment, an instanceof neuron 2402 generating an output to be transmitted over an instanceof synapse 2408 may be referred to as a “pre-synaptic neuron” withrespect to that instance of synapse 2408. In at least one embodiment, aninstance of neuron 2402 receiving an input transmitted over an instanceof synapse 2408 may be referred to as a “post-synaptic neuron” withrespect to that instance of synapse 2408. Because an instance of neuron2402 may receive inputs from one or more instances of synapse 2408, andmay also transmit outputs over one or more instances of synapse 2408, asingle instance of neuron 2402 may therefore be both a “pre-synapticneuron” and “post-synaptic neuron,” with respect to various instances ofsynapses 2408, in at least one embodiment.

In at least one embodiment, neurons 2402 may be organized into one ormore layers. In at least one embodiment, each instance of neuron 2402may have one neuron output 2406 that may fan out through one or moresynapses 2408 to one or more neuron inputs 2404. In at least oneembodiment, neuron outputs 2406 of neurons 2402 in a first layer 2410may be connected to neuron inputs 2404 of neurons 2402 in a second layer2412. In at least one embodiment, layer 2410 may be referred to as a“feed-forward layer.” In at least one embodiment, each instance ofneuron 2402 in an instance of first layer 2410 may fan out to eachinstance of neuron 2402 in second layer 2412. In at least oneembodiment, first layer 2410 may be referred to as a “fully connectedfeed-forward layer.” In at least one embodiment, each instance of neuron2402 in an instance of second layer 2412 may fan out to fewer than allinstances of neuron 2402 in a third layer 2414. In at least oneembodiment, second layer 2412 may be referred to as a “sparselyconnected feed-forward layer.” In at least one embodiment, neurons 2402in second layer 2412 may fan out to neurons 2402 in multiple otherlayers, including to neurons 2402 also in second layer 2412. In at leastone embodiment, second layer 2412 may be referred to as a “recurrentlayer.” In at least one embodiment, neuromorphic processor 2400 mayinclude, without limitation, any suitable combination of recurrentlayers and feed-forward layers, including, without limitation, bothsparsely connected feed-forward layers and fully connected feed-forwardlayers.

In at least one embodiment, neuromorphic processor 2400 may include,without limitation, a reconfigurable interconnect architecture ordedicated hard-wired interconnects to connect synapse 2408 to neurons2402. In at least one embodiment, neuromorphic processor 2400 mayinclude, without limitation, circuitry or logic that allows synapses tobe allocated to different neurons 2402 as needed based on neural networktopology and neuron fan-in/out. For example, in at least one embodiment,synapses 2408 may be connected to neurons 2402 using an interconnectfabric, such as network-on-chip, or with dedicated connections. In atleast one embodiment, synapse interconnections and components thereofmay be implemented using circuitry or logic.

In at least one embodiment, one or more systems depicted in FIG. 24 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 24 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 24 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 25 is a block diagram of a processing system, according to at leastone embodiment. In at least one embodiment, system 2500 includes one ormore processors 2502 and one or more graphics processors 2508, and maybe a single processor desktop system, a multiprocessor workstationsystem, or a server system having a large number of processors 2502 orprocessor cores 2507. In at least one embodiment, system 2500 is aprocessing platform incorporated within a system-on-a-chip (SoC)integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 2500 can include, or be incorporatedwithin a server-based gaming platform, a game console, including a gameand media console, a mobile gaming console, a handheld game console, oran online game console. In at least one embodiment, system 2500 is amobile phone, a smart phone, a tablet computing device or a mobileInternet device. In at least one embodiment, processing system 2500 canalso include, couple with, or be integrated within a wearable device,such as a smart watch wearable device, a smart eyewear device, anaugmented reality device, or a virtual reality device. In at least oneembodiment, processing system 2500 is a television or set top box devicehaving one or more processors 2502 and a graphical interface generatedby one or more graphics processors 2508.

In at least one embodiment, one or more processors 2502 each include oneor more processor cores 2507 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 2507 is configuredto process a specific instruction sequence 2509. In at least oneembodiment, instruction sequence 2509 may facilitate Complex InstructionSet Computing (CISC), Reduced Instruction Set Computing (RISC), orcomputing via a Very Long Instruction Word (VLIW). In at least oneembodiment, processor cores 2507 may each process a differentinstruction sequence 2509, which may include instructions to facilitateemulation of other instruction sequences. In at least one embodiment,processor core 2507 may also include other processing devices, such aDigital Signal Processor (DSP).

In at least one embodiment, processor 2502 includes a cache memory 2504.In at least one embodiment, processor 2502 can have a single internalcache or multiple levels of internal cache. In at least one embodiment,cache memory is shared among various components of processor 2502. In atleast one embodiment, processor 2502 also uses an external cache (e.g.,a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which maybe shared among processor cores 2507 using known cache coherencytechniques. In at least one embodiment, a register file 2506 isadditionally included in processor 2502, which may include differenttypes of registers for storing different types of data (e.g., integerregisters, floating point registers, status registers, and aninstruction pointer register). In at least one embodiment, register file2506 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 2502 are coupledwith one or more interface bus(es) 2510 to transmit communicationsignals such as address, data, or control signals between processor 2502and other components in system 2500. In at least one embodiment,interface bus 2510 can be a processor bus, such as a version of a DirectMedia Interface (DMI) bus. In at least one embodiment, interface bus2510 is not limited to a DMI bus, and may include one or more PeripheralComponent Interconnect buses (e.g., PCI, PCI Express), memory busses, orother types of interface busses. In at least one embodiment processor(s)2502 include an integrated memory controller 2516 and a platformcontroller hub 2530. In at least one embodiment, memory controller 2516facilitates communication between a memory device and other componentsof system 2500, while platform controller hub (PCH) 2530 providesconnections to I/O devices via a local I/O bus.

In at least one embodiment, a memory device 2520 can be a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as process memory. Inat least one embodiment, memory device 2520 can operate as system memoryfor system 2500, to store data 2522 and instructions 2521 for use whenone or more processors 2502 executes an application or process. In atleast one embodiment, memory controller 2516 also couples with anoptional external graphics processor 2512, which may communicate withone or more graphics processors 2508 in processors 2502 to performgraphics and media operations. In at least one embodiment, a displaydevice 2511 can connect to processor(s) 2502. In at least oneembodiment, display device 2511 can include one or more of an internaldisplay device, as in a mobile electronic device or a laptop device, oran external display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 2511 caninclude a head mounted display (HMD) such as a stereoscopic displaydevice for use in virtual reality (VR) applications or augmented reality(AR) applications.

In at least one embodiment, platform controller hub 2530 enablesperipherals to connect to memory device 2520 and processor 2502 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 2546, a network controller2534, a firmware interface 2528, a wireless transceiver 2526, touchsensors 2525, a data storage device 2524 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 2524 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). In at least one embodiment, touch sensors 2525 can includetouch screen sensors, pressure sensors, or fingerprint sensors. In atleast one embodiment, wireless transceiver 2526 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at leastone embodiment, firmware interface 2528 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). In at least one embodiment, network controller 2534can enable a network connection to a wired network. In at least oneembodiment, a high-performance network controller (not shown) coupleswith interface bus 2510. In at least one embodiment, audio controller2546 is a multi-channel high definition audio controller. In at leastone embodiment, system 2500 includes an optional legacy I/O controller2540 for coupling legacy (e.g., Personal System 2 (PS/2)) devices tosystem 2500. In at least one embodiment, platform controller hub 2530can also connect to one or more Universal Serial Bus (USB) controllers2542 connect input devices, such as keyboard and mouse 2543combinations, a camera 2544, or other USB input devices.

In at least one embodiment, an instance of memory controller 2516 andplatform controller hub 2530 may be integrated into a discreet externalgraphics processor, such as external graphics processor 2512. In atleast one embodiment, platform controller hub 2530 and/or memorycontroller 2516 may be external to one or more processor(s) 2502. Forexample, in at least one embodiment, system 2500 can include an externalmemory controller 2516 and platform controller hub 2530, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 2502.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment portions or all of inference and/or training logic 615 may beincorporated into graphics processor 2500. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in a 3D pipeline. Moreover, in at leastone embodiment, inferencing and/or training operations described hereinmay be done using logic other than logic illustrated in FIG. 6A or 6B.In at least one embodiment, weight parameters may be stored in on-chipor off-chip memory and/or registers (shown or not shown) that configureALUs of graphics processor 2500 to perform one or more machine learningalgorithms, neural network architectures, use cases, or trainingtechniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 25 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 25 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 25 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 26 is a block diagram of a processor 2600 having one or moreprocessor cores 2602A-2602N, an integrated memory controller 2614, andan integrated graphics processor 2608, according to at least oneembodiment. In at least one embodiment, processor 2600 can includeadditional cores up to and including additional core 2602N representedby dashed lined boxes. In at least one embodiment, each of processorcores 2602A-2602N includes one or more internal cache units 2604A-2604N.In at least one embodiment, each processor core also has access to oneor more shared cached units 2606.

In at least one embodiment, internal cache units 2604A-2604N and sharedcache units 2606 represent a cache memory hierarchy within processor2600. In at least one embodiment, cache memory units 2604A-2604N mayinclude at least one level of instruction and data cache within eachprocessor core and one or more levels of shared mid-level cache, such asa Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache,where a highest level of cache before external memory is classified asan LLC. In at least one embodiment, cache coherency logic maintainscoherency between various cache units 2606 and 2604A-2604N.

In at least one embodiment, processor 2600 may also include a set of oneor more bus controller units 2616 and a system agent core 2610. In atleast one embodiment, bus controller units 2616 manage a set ofperipheral buses, such as one or more PCI or PCI express busses. In atleast one embodiment, system agent core 2610 provides managementfunctionality for various processor components. In at least oneembodiment, system agent core 2610 includes one or more integratedmemory controllers 2614 to manage access to various external memorydevices (not shown).

In at least one embodiment, one or more of processor cores 2602A-2602Ninclude support for simultaneous multi-threading. In at least oneembodiment, system agent core 2610 includes components for coordinatingand operating cores 2602A-2602N during multi-threaded processing. In atleast one embodiment, system agent core 2610 may additionally include apower control unit (PCU), which includes logic and components toregulate one or more power states of processor cores 2602A-2602N andgraphics processor 2608.

In at least one embodiment, processor 2600 additionally includesgraphics processor 2608 to execute graphics processing operations. In atleast one embodiment, graphics processor 2608 couples with shared cacheunits 2606, and system agent core 2610, including one or more integratedmemory controllers 2614. In at least one embodiment, system agent core2610 also includes a display controller 2611 to drive graphics processoroutput to one or more coupled displays. In at least one embodiment,display controller 2611 may also be a separate module coupled withgraphics processor 2608 via at least one interconnect, or may beintegrated within graphics processor 2608.

In at least one embodiment, a ring-based interconnect unit 2612 is usedto couple internal components of processor 2600. In at least oneembodiment, an alternative interconnect unit may be used, such as apoint-to-point interconnect, a switched interconnect, or othertechniques. In at least one embodiment, graphics processor 2608 coupleswith ring interconnect 2612 via an I/O link 2613.

In at least one embodiment, I/O link 2613 represents at least one ofmultiple varieties of I/O interconnects, including an on package I/Ointerconnect which facilitates communication between various processorcomponents and a high-performance embedded memory module 2618, such asan eDRAM module. In at least one embodiment, each of processor cores2602A-2602N and graphics processor 2608 use embedded memory module 2618as a shared Last Level Cache.

In at least one embodiment, processor cores 2602A-2602N are homogeneouscores executing a common instruction set architecture. In at least oneembodiment, processor cores 2602A-2602N are heterogeneous in terms ofinstruction set architecture (ISA), where one or more of processor cores2602A-2602N execute a common instruction set, while one or more othercores of processor cores 2602A-2602N executes a subset of a commoninstruction set or a different instruction set. In at least oneembodiment, processor cores 2602A-2602N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. In at least one embodiment, processor 2600 can beimplemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment portions or all of inference and/or training logic 615 may beincorporated into graphics processor 2610. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in a 3D pipeline, graphics core(s)2602, shared function logic, or other logic in FIG. 26. Moreover, in atleast one embodiment, inferencing and/or training operations describedherein may be done using logic other than logic illustrated in FIG. 6Aor 6B. In at least one embodiment, weight parameters may be stored inon-chip or off-chip memory and/or registers (shown or not shown) thatconfigure ALUs of processor 2600 to perform one or more machine learningalgorithms, neural network architectures, use cases, or trainingtechniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 26 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 26 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 26 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 27 is a block diagram of a graphics processor 2700, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In at least oneembodiment, graphics processor 2700 communicates via a memory mapped I/Ointerface to registers on graphics processor 2700 and with commandsplaced into memory. In at least one embodiment, graphics processor 2700includes a memory interface 2714 to access memory. In at least oneembodiment, memory interface 2714 is an interface to local memory, oneor more internal caches, one or more shared external caches, and/or tosystem memory.

In at least one embodiment, graphics processor 2700 also includes adisplay controller 2702 to drive display output data to a display device2720. In at least one embodiment, display controller 2702 includeshardware for one or more overlay planes for display device 2720 andcomposition of multiple layers of video or user interface elements. Inat least one embodiment, display device 2720 can be an internal orexternal display device. In at least one embodiment, display device 2720is a head mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In at least oneembodiment, graphics processor 2700 includes a video codec engine 2706to encode, decode, or transcode media to, from, or between one or moremedia encoding formats, including, but not limited to Moving PictureExperts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC)formats such as H.264/MPEG-4 AVC, as well as the Society of MotionPicture & Television Engineers (SMPTE) 421M/VC-1, and Joint PhotographicExperts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG)formats.

In at least one embodiment, graphics processor 2700 includes a blockimage transfer (BLIT) engine 2704 to perform two-dimensional (2D)rasterizer operations including, for example, bit-boundary blocktransfers. However, in at least one embodiment, 2D graphics operationsare performed using one or more components of a graphics processingengine (GPE) 2710. In at least one embodiment, GPE 2710 is a computeengine for performing graphics operations, including three-dimensional(3D) graphics operations and media operations.

In at least one embodiment, GPE 2710 includes a 3D pipeline 2712 forperforming 3D operations, such as rendering three-dimensional images andscenes using processing functions that act upon 3D primitive shapes(e.g., rectangle, triangle, etc.). In at least one embodiment, 3Dpipeline 2712 includes programmable and fixed function elements thatperform various tasks and/or spawn execution threads to a 3D/Mediasub-system 2715. While 3D pipeline 2712 can be used to perform mediaoperations, in at least one embodiment, GPE 2710 also includes a mediapipeline 2716 that is used to perform media operations, such as videopost-processing and image enhancement.

In at least one embodiment, media pipeline 2716 includes fixed functionor programmable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of, video codecengine 2706. In at least one embodiment, media pipeline 2716additionally includes a thread spawning unit to spawn threads forexecution on 3D/Media sub-system 2715. In at least one embodiment,spawned threads perform computations for media operations on one or moregraphics execution units included in 3D/Media sub-system 2715.

In at least one embodiment, 3D/Media subsystem 2715 includes logic forexecuting threads spawned by 3D pipeline 2712 and media pipeline 2716.In at least one embodiment, 3D pipeline 2712 and media pipeline 2716send thread execution requests to 3D/Media subsystem 2715, whichincludes thread dispatch logic for arbitrating and dispatching variousrequests to available thread execution resources. In at least oneembodiment, execution resources include an array of graphics executionunits to process 3D and media threads. In at least one embodiment,3D/Media subsystem 2715 includes one or more internal caches for threadinstructions and data. In at least one embodiment, subsystem 2715 alsoincludes shared memory, including registers and addressable memory, toshare data between threads and to store output data.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment portions or all of inference and/or training logic 615 may beincorporated into graphics processor 2700. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in 3D pipeline 2712. Moreover, in atleast one embodiment, inferencing and/or training operations describedherein may be done using logic other than logic illustrated in FIG. 6Aor 6B. In at least one embodiment, weight parameters may be stored inon-chip or off-chip memory and/or registers (shown or not shown) thatconfigure ALUs of graphics processor 2700 to perform one or more machinelearning algorithms, neural network architectures, use cases, ortraining techniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 27 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 27 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 27 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 28 is a block diagram of a graphics processing engine 2810 of agraphics processor in accordance with at least one embodiment. In atleast one embodiment, graphics processing engine (GPE) 2810 is a versionof GPE 2710 shown in FIG. 27. In at least one embodiment, a mediapipeline 2816 is optional and may not be explicitly included within GPE2810. In at least one embodiment, a separate media and/or imageprocessor is coupled to GPE 2810.

In at least one embodiment, GPE 2810 is coupled to or includes a commandstreamer 2803, which provides a command stream to a 3D pipeline 2812and/or media pipeline 2816. In at least one embodiment, command streamer2803 is coupled to memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In at least oneembodiment, command streamer 2803 receives commands from memory andsends commands to 3D pipeline 2812 and/or media pipeline 2816. In atleast one embodiment, commands are instructions, primitives, ormicro-operations fetched from a ring buffer, which stores commands for3D pipeline 2812 and media pipeline 2816. In at least one embodiment, aring buffer can additionally include batch command buffers storingbatches of multiple commands. In at least one embodiment, commands for3D pipeline 2812 can also include references to data stored in memory,such as, but not limited to, vertex and geometry data for 3D pipeline2812 and/or image data and memory objects for media pipeline 2816. In atleast one embodiment, 3D pipeline 2812 and media pipeline 2816 processcommands and data by performing operations or by dispatching one or moreexecution threads to a graphics core array 2814. In at least oneembodiment, graphics core array 2814 includes one or more blocks ofgraphics cores (e.g., graphics core(s) 2815A, graphics core(s) 2815B),each block including one or more graphics cores. In at least oneembodiment, each graphics core includes a set of graphics executionresources that includes general-purpose and graphics specific executionlogic to perform graphics and compute operations, as well as fixedfunction texture processing and/or machine learning and artificialintelligence acceleration logic, including inference and/or traininglogic 615 in FIG. 6A and FIG. 6B.

In at least one embodiment, 3D pipeline 2812 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing instructionsand dispatching execution threads to graphics core array 2814. In atleast one embodiment, graphics core array 2814 provides a unified blockof execution resources for use in processing shader programs. In atleast one embodiment, a multi-purpose execution logic (e.g., executionunits) within graphics core(s) 2815A-2815B of graphic core array 2814includes support for various 3D API shader languages and can executemultiple simultaneous execution threads associated with multipleshaders.

In at least one embodiment, graphics core array 2814 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In at least one embodiment, execution units additionallyinclude general-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations.

In at least one embodiment, output data generated by threads executingon graphics core array 2814 can output data to memory in a unifiedreturn buffer (URB) 2818. In at least one embodiment, URB 2818 can storedata for multiple threads. In at least one embodiment, URB 2818 may beused to send data between different threads executing on graphics corearray 2814. In at least one embodiment, URB 2818 may additionally beused for synchronization between threads on graphics core array 2814 andfixed function logic within shared function logic 2820.

In at least one embodiment, graphics core array 2814 is scalable, suchthat graphics core array 2814 includes a variable number of graphicscores, each having a variable number of execution units based on atarget power and performance level of GPE 2810. In at least oneembodiment, execution resources are dynamically scalable, such thatexecution resources may be enabled or disabled as needed.

In at least one embodiment, graphics core array 2814 is coupled toshared function logic 2820 that includes multiple resources that areshared between graphics cores in graphics core array 2814. In at leastone embodiment, shared functions performed by shared function logic 2820are embodied in hardware logic units that provide specializedsupplemental functionality to graphics core array 2814. In at least oneembodiment, shared function logic 2820 includes but is not limited to asampler unit 2821, a math unit 2822, and inter-thread communication(ITC) logic 2823. In at least one embodiment, one or more cache(s) 2825are included in, or coupled to, shared function logic 2820.

In at least one embodiment, a shared function is used if demand for aspecialized function is insufficient for inclusion within graphics corearray 2814. In at least one embodiment, a single instantiation of aspecialized function is used in shared function logic 2820 and sharedamong other execution resources within graphics core array 2814. In atleast one embodiment, specific shared functions within shared functionlogic 2820 that are used extensively by graphics core array 2814 may beincluded within shared function logic 3116 within graphics core array2814. In at least one embodiment, shared function logic 3116 withingraphics core array 2814 can include some or all logic within sharedfunction logic 2820. In at least one embodiment, all logic elementswithin shared function logic 2820 may be duplicated within sharedfunction logic 2826 of graphics core array 2814. In at least oneembodiment, shared function logic 2820 is excluded in favor of sharedfunction logic 2826 within graphics core array 2814.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment portions or all of inference and/or training logic 615 may beincorporated into graphics processor 2810. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in 3D pipeline 2812, graphics core(s)2815, shared function logic 2826, shared function logic 2820, or otherlogic in FIG. 28. Moreover, in at least one embodiment, inferencingand/or training operations described herein may be done using logicother than logic illustrated in FIG. 6A or 6B. In at least oneembodiment, weight parameters may be stored in on-chip or off-chipmemory and/or registers (shown or not shown) that configure ALUs ofgraphics processor 2810 to perform one or more machine learningalgorithms, neural network architectures, use cases, or trainingtechniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 28 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 28 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 28 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 29 is a block diagram of hardware logic of a graphics processorcore 2900, according to at least one embodiment described herein. In atleast one embodiment, graphics processor core 2900 is included within agraphics core array. In at least one embodiment, graphics processor core2900, sometimes referred to as a core slice, can be one or multiplegraphics cores within a modular graphics processor. In at least oneembodiment, graphics processor core 2900 is exemplary of one graphicscore slice, and a graphics processor as described herein may includemultiple graphics core slices based on target power and performanceenvelopes. In at least one embodiment, each graphics core 2900 caninclude a fixed function block 2930 coupled with multiple sub-cores2901A-2901F, also referred to as sub-slices, that include modular blocksof general-purpose and fixed function logic.

In at least one embodiment, fixed function block 2930 includes ageometry and fixed function pipeline 2936 that can be shared by allsub-cores in graphics processor 2900, for example, in lower performanceand/or lower power graphics processor implementations. In at least oneembodiment, geometry and fixed function pipeline 2936 includes a 3Dfixed function pipeline, a video front-end unit, a thread spawner andthread dispatcher, and a unified return buffer manager, which managesunified return buffers.

In at least one embodiment, fixed function block 2930 also includes agraphics SoC interface 2937, a graphics microcontroller 2938, and amedia pipeline 2939. In at least one embodiment, graphics SoC interface2937 provides an interface between graphics core 2900 and otherprocessor cores within a system on a chip integrated circuit. In atleast one embodiment, graphics microcontroller 2938 is a programmablesub-processor that is configurable to manage various functions ofgraphics processor 2900, including thread dispatch, scheduling, andpre-emption. In at least one embodiment, media pipeline 2939 includeslogic to facilitate decoding, encoding, pre-processing, and/orpost-processing of multimedia data, including image and video data. Inat least one embodiment, media pipeline 2939 implements media operationsvia requests to compute or sampling logic within sub-cores 2901A-2901F.

In at least one embodiment, SoC interface 2937 enables graphics core2900 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, system RAM,and/or embedded on-chip or on-package DRAM. In at least one embodiment,SoC interface 2937 can also enable communication with fixed functiondevices within an SoC, such as camera imaging pipelines, and enables useof and/or implements global memory atomics that may be shared betweengraphics core 2900 and CPUs within an SoC. In at least one embodiment,graphics SoC interface 2937 can also implement power management controlsfor graphics processor core 2900 and enable an interface between a clockdomain of graphics processor core 2900 and other clock domains within anSoC. In at least one embodiment, SoC interface 2937 enables receipt ofcommand buffers from a command streamer and global thread dispatcherthat are configured to provide commands and instructions to each of oneor more graphics cores within a graphics processor. In at least oneembodiment, commands and instructions can be dispatched to mediapipeline 2939, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline2936, and/or a geometry and fixed function pipeline 2914) when graphicsprocessing operations are to be performed.

In at least one embodiment, graphics microcontroller 2938 can beconfigured to perform various scheduling and management tasks forgraphics core 2900. In at least one embodiment, graphics microcontroller2938 can perform graphics and/or compute workload scheduling on variousgraphics parallel engines within execution unit (EU) arrays 2902A-2902F,2904A-2904F within sub-cores 2901A-2901F. In at least one embodiment,host software executing on a CPU core of an SoC including graphics core2900 can submit workloads to one of multiple graphic processor paths,which invokes a scheduling operation on an appropriate graphics engine.In at least one embodiment, scheduling operations include determiningwhich workload to run next, submitting a workload to a command streamer,pre-empting existing workloads running on an engine, monitoring progressof a workload, and notifying host software when a workload is complete.In at least one embodiment, graphics microcontroller 2938 can alsofacilitate low-power or idle states for graphics core 2900, providinggraphics core 2900 with an ability to save and restore registers withingraphics core 2900 across low-power state transitions independently froman operating system and/or graphics driver software on a system.

In at least one embodiment, graphics core 2900 may have greater than orfewer than illustrated sub-cores 2901A-2901F, up to N modular sub-cores.For each set of N sub-cores, in at least one embodiment, graphics core2900 can also include shared function logic 2910, shared and/or cachememory 2912, geometry/fixed function pipeline 2914, as well asadditional fixed function logic 2916 to accelerate various graphics andcompute processing operations. In at least one embodiment, sharedfunction logic 2910 can include logic units (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin graphics core 2900. In at least one embodiment, shared and/orcache memory 2912 can be a last-level cache for N sub-cores 2901A-2901Fwithin graphics core 2900 and can also serve as shared memory that isaccessible by multiple sub-cores. In at least one embodiment,geometry/fixed function pipeline 2914 can be included instead ofgeometry/fixed function pipeline 2936 within fixed function block 2930and can include similar logic units.

In at least one embodiment, graphics core 2900 includes additional fixedfunction logic 2916 that can include various fixed function accelerationlogic for use by graphics core 2900. In at least one embodiment,additional fixed function logic 2916 includes an additional geometrypipeline for use in position-only shading. In position-only shading, atleast two geometry pipelines exist, whereas in a full geometry pipelinewithin geometry and fixed function pipelines 2914, 2936, and a cullpipeline, which is an additional geometry pipeline that may be includedwithin additional fixed function logic 2916. In at least one embodiment,a cull pipeline is a trimmed down version of a full geometry pipeline.In at least one embodiment, a full pipeline and a cull pipeline canexecute different instances of an application, each instance having aseparate context. In at least one embodiment, position only shading canhide long cull runs of discarded triangles, enabling shading to becompleted earlier in some instances. For example, in at least oneembodiment, cull pipeline logic within additional fixed function logic2916 can execute position shaders in parallel with a main applicationand generally generates critical results faster than a full pipeline, asa cull pipeline fetches and shades position attributes of vertices,without performing rasterization and rendering of pixels to a framebuffer. In at least one embodiment, a cull pipeline can use generatedcritical results to compute visibility information for all triangleswithout regard to whether those triangles are culled. In at least oneembodiment, a full pipeline (which in this instance may be referred toas a replay pipeline) can consume visibility information to skip culledtriangles to shade only visible triangles that are finally passed to arasterization phase.

In at least one embodiment, additional fixed function logic 2916 canalso include machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

In at least one embodiment, within each graphics sub-core 2901A-2901Fincludes a set of execution resources that may be used to performgraphics, media, and compute operations in response to requests bygraphics pipeline, media pipeline, or shader programs. In at least oneembodiment, graphics sub-cores 2901A-2901F include multiple EU arrays2902A-2902F, 2904A-2904F, thread dispatch and inter-thread communication(TD/IC) logic 2903A-2903F, a 3D (e.g., texture) sampler 2905A-2905F, amedia sampler 2906A-2906F, a shader processor 2907A-2907F, and sharedlocal memory (SLM) 2908A-2908F. In at least one embodiment, EU arrays2902A-2902F, 2904A-2904F each include multiple execution units, whichare general-purpose graphics processing units capable of performingfloating-point and integer/fixed-point logic operations in service of agraphics, media, or compute operation, including graphics, media, orcompute shader programs. In at least one embodiment, TD/IC logic2903A-2903F performs local thread dispatch and thread control operationsfor execution units within a sub-core and facilitates communicationbetween threads executing on execution units of a sub-core. In at leastone embodiment, 3D samplers 2905A-2905F can read texture or other 3Dgraphics related data into memory. In at least one embodiment, 3Dsamplers can read texture data differently based on a configured samplestate and texture format associated with a given texture. In at leastone embodiment, media samplers 2906A-2906F can perform similar readoperations based on a type and format associated with media data. In atleast one embodiment, each graphics sub-core 2901A-2901F can alternatelyinclude a unified 3D and media sampler. In at least one embodiment,threads executing on execution units within each of sub-cores2901A-2901F can make use of shared local memory 2908A-2908F within eachsub-core, to enable threads executing within a thread group to executeusing a common pool of on-chip memory.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, portions or all of inference and/or training logic 615 maybe incorporated into graphics processor 2910. For example, in at leastone embodiment, training and/or inferencing techniques described hereinmay use one or more of ALUs embodied in a 3D pipeline, graphicsmicrocontroller 2938, geometry and fixed function pipeline 2914 and2936, or other logic in FIG. 29. Moreover, in at least one embodiment,inferencing and/or training operations described herein may be doneusing logic other than logic illustrated in FIG. 6A or 6B. In at leastone embodiment, weight parameters may be stored in on-chip or off-chipmemory and/or registers (shown or not shown) that configure ALUs ofgraphics processor 2900 to perform one or more machine learningalgorithms, neural network architectures, use cases, or trainingtechniques described herein.

In at least one embodiment, one or more systems depicted in FIG. 29 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 29 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 29 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIGS. 30A-30B illustrate thread execution logic 3000 including an arrayof processing elements of a graphics processor core according to atleast one embodiment. FIG. 30A illustrates at least one embodiment, inwhich thread execution logic 3000 is used. FIG. 30B illustratesexemplary internal details of a graphics execution unit 3008, accordingto at least one embodiment.

As illustrated in FIG. 30A, in at least one embodiment, thread executionlogic 3000 includes a shader processor 3002, a thread dispatcher 3004,an instruction cache 3006, a scalable execution unit array including aplurality of execution units 3007A-3007N and 3008A-3008N, a sampler3010, a data cache 3012, and a data port 3014. In at least oneembodiment, a scalable execution unit array can dynamically scale byenabling or disabling one or more execution units (e.g., any ofexecution unit 3008A-N or 3007A-N) based on computational requirementsof a workload, for example. In at least one embodiment, scalableexecution units are interconnected via an interconnect fabric that linksto each execution unit. In at least one embodiment, thread executionlogic 3000 includes one or more connections to memory, such as systemmemory or cache memory, through one or more of instruction cache 3006,data port 3014, sampler 3010, and execution units 3007 or 3008. In atleast one embodiment, each execution unit (e.g., 3007A) is a stand-aloneprogrammable general-purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In at least oneembodiment, array of execution units 3007 and/or 3008 is scalable toinclude any number individual execution units.

In at least one embodiment, execution units 3007 and/or 3008 areprimarily used to execute shader programs. In at least one embodiment,shader processor 3002 can process various shader programs and dispatchexecution threads associated with shader programs via a threaddispatcher 3004. In at least one embodiment, thread dispatcher 3004includes logic to arbitrate thread initiation requests from graphics andmedia pipelines and instantiate requested threads on one or moreexecution units in execution units 3007 and/or 3008. For example, in atleast one embodiment, a geometry pipeline can dispatch vertex,tessellation, or geometry shaders to thread execution logic forprocessing. In at least one embodiment, thread dispatcher 3004 can alsoprocess runtime thread spawning requests from executing shader programs.

In at least one embodiment, execution units 3007 and/or 3008 support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. In at least one embodiment, execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,and/or vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders). In at least one embodiment, each of execution units 3007and/or 3008, which include one or more arithmetic logic units (ALUs), iscapable of multi-issue single instruction multiple data (SIMD) executionand multi-threaded operation enables an efficient execution environmentdespite higher latency memory accesses. In at least one embodiment, eachhardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state. Inat least one embodiment, execution is multi-issue per clock to pipelinescapable of integer, single and double precision floating pointoperations, SIMD branch capability, logical operations, transcendentaloperations, and other miscellaneous operations. In at least oneembodiment, while waiting for data from memory or one of sharedfunctions, dependency logic within execution units 3007 and/or 3008causes a waiting thread to sleep until requested data has been returned.In at least one embodiment, while an awaiting thread is sleeping,hardware resources may be devoted to processing other threads. Forexample, in at least one embodiment, during a delay associated with avertex shader operation, an execution unit can perform operations for apixel shader, fragment shader, or another type of shader program,including a different vertex shader.

In at least one embodiment, each execution unit in execution units 3007and/or 3008 operates on arrays of data elements. In at least oneembodiment, a number of data elements is an “execution size,” or numberof channels for an instruction. In at least one embodiment, an executionchannel is a logical unit of execution for data element access, masking,and flow control within instructions. In at least one embodiment, anumber of channels may be independent of a number of physical arithmeticlogic units (ALUs) or floating point units (FPUs) for a particulargraphics processor. In at least one embodiment, execution units 3007and/or 3008 support integer and floating-point data types.

In at least one embodiment, an execution unit instruction set includesSIMD instructions. In at least one embodiment, various data elements canbe stored as a packed data type in a register and execution unit willprocess various elements based on data size of elements. For example, inat least one embodiment, when operating on a 256-bit wide vector, 256bits of a vector are stored in a register and an execution unit operateson a vector as four separate 64-bit packed data elements (Quad-Word (QW)size data elements), eight separate 32-bit packed data elements (DoubleWord (DW) size data elements), sixteen separate 16-bit packed dataelements (Word (W) size data elements), or thirty-two separate 8-bitdata elements (byte (B) size data elements). However, in at least oneembodiment, different vector widths and register sizes are possible.

In at least one embodiment, one or more execution units can be combinedinto a fused execution unit 3009A-3009N having thread control logic(3011A-3011N) that is common to fused EUs such as execution unit 3007Afused with execution unit 3008A into fused execution unit 3009A. In atleast one embodiment, multiple EUs can be fused into an EU group. In atleast one embodiment, each EU in a fused EU group can be configured toexecute a separate SIMD hardware thread, with a number of EUs in a fusedEU group possibly varying according to various embodiments. In at leastone embodiment, various SIMD widths can be performed per-EU, includingbut not limited to SIMD8, SIMD16, and SIMD32. In at least oneembodiment, each fused graphics execution unit 3009A-3009N includes atleast two execution units. For example, in at least one embodiment,fused execution unit 3009A includes a first EU 3007A, second EU 3008A,and thread control logic 3011A that is common to first EU 3007A andsecond EU 3008A. In at least one embodiment, thread control logic 3011Acontrols threads executed on fused graphics execution unit 3009A,allowing each EU within fused execution units 3009A-3009N to executeusing a common instruction pointer register.

In at least one embodiment, one or more internal instruction caches(e.g., 3006) are included in thread execution logic 3000 to cache threadinstructions for execution units. In at least one embodiment, one ormore data caches (e.g., 3012) are included to cache thread data duringthread execution. In at least one embodiment, sampler 3010 is includedto provide texture sampling for 3D operations and media sampling formedia operations. In at least one embodiment, sampler 3010 includesspecialized texture or media sampling functionality to process textureor media data during sampling process before providing sampled data toan execution unit.

During execution, in at least one embodiment, graphics and mediapipelines send thread initiation requests to thread execution logic 3000via thread spawning and dispatch logic. In at least one embodiment, oncea group of geometric objects has been processed and rasterized intopixel data, pixel processor logic (e.g., pixel shader logic, fragmentshader logic, etc.) within shader processor 3002 is invoked to furthercompute output information and cause results to be written to outputsurfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). Inat least one embodiment, a pixel shader or a fragment shader calculatesvalues of various vertex attributes that are to be interpolated across arasterized object. In at least one embodiment, pixel processor logicwithin shader processor 3002 then executes an application programminginterface (API)-supplied pixel or fragment shader program. In at leastone embodiment, to execute a shader program, shader processor 3002dispatches threads to an execution unit (e.g., 3008A) via threaddispatcher 3004. In at least one embodiment, shader processor 3002 usestexture sampling logic in sampler 3010 to access texture data in texturemaps stored in memory. In at least one embodiment, arithmetic operationson texture data and input geometry data compute pixel color data foreach geometric fragment, or discards one or more pixels from furtherprocessing.

In at least one embodiment, data port 3014 provides a memory accessmechanism for thread execution logic 3000 to output processed data tomemory for further processing on a graphics processor output pipeline.In at least one embodiment, data port 3014 includes or couples to one ormore cache memories (e.g., data cache 3012) to cache data for memoryaccess via a data port.

As illustrated in FIG. 30B, in at least one embodiment, a graphicsexecution unit 3008 can include an instruction fetch unit 3037, ageneral register file array (GRF) 3024, an architectural register filearray (ARF) 3026, a thread arbiter 3022, a send unit 3030, a branch unit3032, a set of SIMD floating point units (FPUs) 3034, and a set ofdedicated integer SIMD ALUs 3035. In at least one embodiment, GRF 3024and ARF 3026 includes a set of general register files and architectureregister files associated with each simultaneous hardware thread thatmay be active in graphics execution unit 3008. In at least oneembodiment, per thread architectural state is maintained in ARF 3026,while data used during thread execution is stored in GRF 3024. In atleast one embodiment, execution state of each thread, includinginstruction pointers for each thread, can be held in thread-specificregisters in ARF 3026.

In at least one embodiment, graphics execution unit 3008 has anarchitecture that is a combination of Simultaneous Multi-Threading (SMT)and fine-grained Interleaved Multi-Threading (IMT). In at least oneembodiment, architecture has a modular configuration that can befine-tuned at design time based on a target number of simultaneousthreads and number of registers per execution unit, where execution unitresources are divided across logic used to execute multiple simultaneousthreads.

In at least one embodiment, graphics execution unit 3008 can co-issuemultiple instructions, which may each be different instructions. In atleast one embodiment, thread arbiter 3022 of graphics execution unitthread 3008 can dispatch instructions to one of send unit 3030, branchunit 3032, or SIMD FPU(s) 3034 for execution. In at least oneembodiment, each execution thread can access 128 general-purposeregisters within GRF 3024, where each register can store 32 bytes,accessible as a SIMD 8-element vector of 32-bit data elements. In atleast one embodiment, each execution unit thread has access to 4kilobytes within GRF 3024, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In at least one embodiment, up to seven threads can executesimultaneously, although a number of threads per execution unit can alsovary according to embodiments. In at least one embodiment, in whichseven threads may access 4 kilobytes, GRF 3024 can store a total of 28kilobytes. In at least one embodiment, flexible addressing modes canpermit registers to be addressed together to build effectively widerregisters or to represent strided rectangular block data structures.

In at least one embodiment, memory operations, sampler operations, andother longer-latency system communications are dispatched via “send”instructions that are executed by message passing to send unit 3030. Inat least one embodiment, branch instructions are dispatched to branchunit 3032 to facilitate SIMD divergence and eventual convergence.

In at least one embodiment, graphics execution unit 3008 includes one ormore SIMD floating point units (FPU(s)) 3034 to perform floating-pointoperations. In at least one embodiment, FPU(s) 3034 also support integercomputation. In at least one embodiment, FPU(s) 3034 can SIMD execute upto M number of 32-bit floating-point (or integer) operations, or SIMDexecute up to 2M 16-bit integer or 16-bit floating-point operations. Inat least one embodiment, at least one FPU provides extended mathcapability to support high-throughput transcendental math functions anddouble precision 64-bit floating-point. In at least one embodiment, aset of 8-bit integer SIMD ALUs 3035 are also present, and may bespecifically optimized to perform operations associated with machinelearning computations.

In at least one embodiment, arrays of multiple instances of graphicsexecution unit 3008 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). In at least one embodiment, execution unit 3008 canexecute instructions across a plurality of execution channels. In atleast one embodiment, each thread executed on graphics execution unit3008 is executed on a different channel.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, portions or all of inference and/or training logic 615 maybe incorporated into thread execution logic 3000. Moreover, in at leastone embodiment, inferencing and/or training operations described hereinmay be done using logic other than logic illustrated in FIG. 6A or 6B.In at least one embodiment, weight parameters may be stored in on-chipor off-chip memory and/or registers (shown or not shown) that configureALUs thread of execution logic 3000 to perform one or more machinelearning algorithms, neural network architectures, use cases, ortraining techniques described herein.

In at least one embodiment, one or more systems depicted in FIG.30A-FIG. 30B are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 30A-FIG. 30B are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.30A-FIG. 30B are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIG. 31 illustrates a parallel processing unit (“PPU”) 3100, accordingto at least one embodiment. In at least one embodiment, PPU 3100 isconfigured with machine-readable code that, if executed by PPU 3100,causes PPU 3100 to perform some or all of processes and techniquesdescribed throughout this disclosure. In at least one embodiment, PPU3100 is a multi-threaded processor that is implemented on one or moreintegrated circuit devices and that utilizes multithreading as alatency-hiding technique designed to process computer-readableinstructions (also referred to as machine-readable instructions orsimply instructions) on multiple threads in parallel. In at least oneembodiment, a thread refers to a thread of execution and is aninstantiation of a set of instructions configured to be executed by PPU3100. In at least one embodiment, PPU 3100 is a graphics processing unit(“GPU”) configured to implement a graphics rendering pipeline forprocessing three-dimensional (“3D”) graphics data in order to generatetwo-dimensional (“2D”) image data for display on a display device suchas a liquid crystal display (“LCD”) device. In at least one embodiment,PPU 3100 is utilized to perform computations such as linear algebraoperations and machine-learning operations. FIG. 31 illustrates anexample parallel processor for illustrative purposes only and should beconstrued as a non-limiting example of processor architecturescontemplated within scope of this disclosure and that any suitableprocessor may be employed to supplement and/or substitute for same.

In at least one embodiment, one or more PPUs 3100 are configured toaccelerate High Performance Computing (“HPC”), data center, and machinelearning applications. In at least one embodiment, PPU 3100 isconfigured to accelerate deep learning systems and applicationsincluding following non-limiting examples: autonomous vehicle platforms,deep learning, high-accuracy speech, image, text recognition systems,intelligent video analytics, molecular simulations, drug discovery,disease diagnosis, weather forecasting, big data analytics, astronomy,molecular dynamics simulation, financial modeling, robotics, factoryautomation, real-time language translation, online search optimizations,and personalized user recommendations, and more.

In at least one embodiment, PPU 3100 includes, without limitation, anInput/Output (“I/O”) unit 3106, a front-end unit 3110, a scheduler unit3112, a work distribution unit 3114, a hub 3116, a crossbar (“XBar”)3120, one or more general processing clusters (“GPCs”) 3118, and one ormore partition units (“memory partition units”) 3122. In at least oneembodiment, PPU 3100 is connected to a host processor or other PPUs 3100via one or more high-speed GPU interconnects (“GPU interconnects”) 3108.In at least one embodiment, PPU 3100 is connected to a host processor orother peripheral devices via a system bus 3102. In at least oneembodiment, PPU 3100 is connected to a local memory comprising one ormore memory devices (“memory”) 3104. In at least one embodiment, memorydevices 3104 include, without limitation, one or more dynamic randomaccess memory (“DRAM”) devices. In at least one embodiment, one or moreDRAM devices are configured and/or configurable as high-bandwidth memory(“HBM”) subsystems, with multiple DRAM dies stacked within each device.

In at least one embodiment, high-speed GPU interconnect 3108 may referto a wire-based multi-lane communications link that is used by systemsto scale and include one or more PPUs 3100 combined with one or morecentral processing units (“CPUs”), supports cache coherence between PPUs3100 and CPUs, and CPU mastering. In at least one embodiment, dataand/or commands are transmitted by high-speed GPU interconnect 3108through hub 3116 to/from other units of PPU 3100 such as one or morecopy engines, video encoders, video decoders, power management units,and other components which may not be explicitly illustrated in FIG. 31.

In at least one embodiment, I/O unit 3106 is configured to transmit andreceive communications (e.g., commands, data) from a host processor (notillustrated in FIG. 31) over system bus 3102. In at least oneembodiment, I/O unit 3106 communicates with host processor directly viasystem bus 3102 or through one or more intermediate devices such as amemory bridge. In at least one embodiment, I/O unit 3106 may communicatewith one or more other processors, such as one or more of PPUs 3100 viasystem bus 3102. In at least one embodiment, I/O unit 3106 implements aPeripheral Component Interconnect Express (“PCIe”) interface forcommunications over a PCIe bus. In at least one embodiment, I/O unit3106 implements interfaces for communicating with external devices.

In at least one embodiment, I/O unit 3106 decodes packets received viasystem bus 3102. In at least one embodiment, at least some packetsrepresent commands configured to cause PPU 3100 to perform variousoperations. In at least one embodiment, I/O unit 3106 transmits decodedcommands to various other units of PPU 3100 as specified by commands. Inat least one embodiment, commands are transmitted to front-end unit 3110and/or transmitted to hub 3116 or other units of PPU 3100 such as one ormore copy engines, a video encoder, a video decoder, a power managementunit, etc. (not explicitly illustrated in FIG. 31). In at least oneembodiment, I/O unit 3106 is configured to route communications betweenand among various logical units of PPU 3100.

In at least one embodiment, a program executed by host processor encodesa command stream in a buffer that provides workloads to PPU 3100 forprocessing. In at least one embodiment, a workload comprisesinstructions and data to be processed by those instructions. In at leastone embodiment, a buffer is a region in a memory that is accessible(e.g., read/write) by both a host processor and PPU 3100—a hostinterface unit may be configured to access that buffer in a systemmemory connected to system bus 3102 via memory requests transmitted oversystem bus 3102 by I/O unit 3106. In at least one embodiment, a hostprocessor writes a command stream to a buffer and then transmits apointer to a start of a command stream to PPU 3100 such that front-endunit 3110 receives pointers to one or more command streams and managesone or more command streams, reading commands from command streams andforwarding commands to various units of PPU 3100.

In at least one embodiment, front-end unit 3110 is coupled to schedulerunit 3112 that configures various GPCs 3118 to process tasks defined byone or more command streams. In at least one embodiment, scheduler unit3112 is configured to track state information related to various tasksmanaged by scheduler unit 3112 where state information may indicatewhich of GPCs 3118 a task is assigned to, whether task is active orinactive, a priority level associated with task, and so forth. In atleast one embodiment, scheduler unit 3112 manages execution of aplurality of tasks on one or more of GPCs 3118.

In at least one embodiment, scheduler unit 3112 is coupled to workdistribution unit 3114 that is configured to dispatch tasks forexecution on GPCs 3118. In at least one embodiment, work distributionunit 3114 tracks a number of scheduled tasks received from schedulerunit 3112 and work distribution unit 3114 manages a pending task pooland an active task pool for each of GPCs 3118. In at least oneembodiment, pending task pool comprises a number of slots (e.g., 32slots) that contain tasks assigned to be processed by a particular GPC3118; an active task pool may comprise a number of slots (e.g., 4 slots)for tasks that are actively being processed by GPCs 3118 such that asone of GPCs 3118 completes execution of a task, that task is evictedfrom that active task pool for GPC 3118 and another task from a pendingtask pool is selected and scheduled for execution on GPC 3118. In atleast one embodiment, if an active task is idle on GPC 3118, such aswhile waiting for a data dependency to be resolved, then that activetask is evicted from GPC 3118 and returned to that pending task poolwhile another task in that pending task pool is selected and scheduledfor execution on GPC 3118.

In at least one embodiment, work distribution unit 3114 communicateswith one or more GPCs 3118 via XBar 3120. In at least one embodiment,XBar 3120 is an interconnect network that couples many of units of PPU3100 to other units of PPU 3100 and can be configured to couple workdistribution unit 3114 to a particular GPC 3118. In at least oneembodiment, one or more other units of PPU 3100 may also be connected toXBar 3120 via hub 3116.

In at least one embodiment, tasks are managed by scheduler unit 3112 anddispatched to one of GPCs 3118 by work distribution unit 3114. In atleast one embodiment, GPC 3118 is configured to process task andgenerate results. In at least one embodiment, results may be consumed byother tasks within GPC 3118, routed to a different GPC 3118 via XBar3120, or stored in memory 3104. In at least one embodiment, results canbe written to memory 3104 via partition units 3122, which implement amemory interface for reading and writing data to/from memory 3104. In atleast one embodiment, results can be transmitted to another PPU 3104 orCPU via high-speed GPU interconnect 3108. In at least one embodiment,PPU 3100 includes, without limitation, a number U of partition units3122 that is equal to a number of separate and distinct memory devices3104 coupled to PPU 3100, as described in more detail herein inconjunction with FIG. 33.

In at least one embodiment, a host processor executes a driver kernelthat implements an application programming interface (“API”) thatenables one or more applications executing on a host processor toschedule operations for execution on PPU 3100. In at least oneembodiment, multiple compute applications are simultaneously executed byPPU 3100 and PPU 3100 provides isolation, quality of service (“QoS”),and independent address spaces for multiple compute applications. In atleast one embodiment, an application generates instructions (e.g., inform of API calls) that cause a driver kernel to generate one or moretasks for execution by PPU 3100 and that driver kernel outputs tasks toone or more streams being processed by PPU 3100. In at least oneembodiment, each task comprises one or more groups of related threads,which may be referred to as a warp. In at least one embodiment, a warpcomprises a plurality of related threads (e.g., 32 threads) that can beexecuted in parallel. In at least one embodiment, cooperating threadscan refer to a plurality of threads including instructions to performtask and that exchange data through shared memory. In at least oneembodiment, threads and cooperating threads are described in more detailin conjunction with FIG. 33.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, deep learning application processor is used to train amachine learning model, such as a neural network, to predict or inferinformation provided to PPU 3100. In at least one embodiment, deeplearning application processor 3100 is used to infer or predictinformation based on a trained machine learning model (e.g., neuralnetwork) that has been trained by another processor or system or by PPU3100. In at least one embodiment, PPU 3100 may be used to perform one ormore neural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 31 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 31 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 31 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 32 illustrates a general processing cluster (“GPC”) 3200, accordingto at least one embodiment. In at least one embodiment, GPC 3200 is GPC3118 of FIG. 31. In at least one embodiment, each GPC 3200 includes,without limitation, a number of hardware units for processing tasks andeach GPC 3200 includes, without limitation, a pipeline manager 3202, apre-raster operations unit (“preROP”) 3204, a raster engine 3208, a workdistribution crossbar (“WDX”) 3216, a memory management unit (“MMU”)3218, one or more Data Processing Clusters (“DPCs”) 3206, and anysuitable combination of parts.

In at least one embodiment, operation of GPC 3200 is controlled bypipeline manager 3202. In at least one embodiment, pipeline manager 3202manages configuration of one or more DPCs 3206 for processing tasksallocated to GPC 3200. In at least one embodiment, pipeline manager 3202configures at least one of one or more DPCs 3206 to implement at least aportion of a graphics rendering pipeline. In at least one embodiment,DPC 3206 is configured to execute a vertex shader program on aprogrammable streaming multi-processor (“SM”) 3214. In at least oneembodiment, pipeline manager 3202 is configured to route packetsreceived from a work distribution unit to appropriate logical unitswithin GPC 3200, in at least one embodiment, and some packets may berouted to fixed function hardware units in preROP 3204 and/or rasterengine 3208 while other packets may be routed to DPCs 3206 forprocessing by a primitive engine 3212 or SM 3214. In at least oneembodiment, pipeline manager 3202 configures at least one of DPCs 3206to implement a neural network model and/or a computing pipeline.

In at least one embodiment, preROP unit 3204 is configured, in at leastone embodiment, to route data generated by raster engine 3208 and DPCs3206 to a Raster Operations (“ROP”) unit in partition unit 3122,described in more detail above in conjunction with FIG. 31. In at leastone embodiment, preROP unit 3204 is configured to perform optimizationsfor color blending, organize pixel data, perform address translations,and more. In at least one embodiment, raster engine 3208 includes,without limitation, a number of fixed function hardware units configuredto perform various raster operations, in at least one embodiment, andraster engine 3208 includes, without limitation, a setup engine, acoarse raster engine, a culling engine, a clipping engine, a fine rasterengine, a tile coalescing engine, and any suitable combination thereof.In at least one embodiment, setup engine receives transformed verticesand generates plane equations associated with geometric primitivedefined by vertices; plane equations are transmitted to a coarse rasterengine to generate coverage information (e.g., an x, y coverage mask fora tile) for primitive; output of a coarse raster engine is transmittedto a culling engine where fragments associated with a primitive thatfail a z-test are culled, and transmitted to a clipping engine wherefragments lying outside a viewing frustum are clipped. In at least oneembodiment, fragments that survive clipping and culling are passed to afine raster engine to generate attributes for pixel fragments based onplane equations generated by a setup engine. In at least one embodiment,an output of raster engine 3208 comprises fragments to be processed byany suitable entity, such as by a fragment shader implemented within DPC3206.

In at least one embodiment, each DPC 3206 included in GPC 3200comprises, without limitation, an M-Pipe Controller (“MPC”) 3210;primitive engine 3212; one or more SMs 3214; and any suitablecombination thereof. In at least one embodiment, MPC 3210 controlsoperation of DPC 3206, routing packets received from pipeline manager3202 to appropriate units in DPC 3206. In at least one embodiment,packets associated with a vertex are routed to primitive engine 3212,which is configured to fetch vertex attributes associated with a vertexfrom memory; in contrast, packets associated with a shader program maybe transmitted to SM 3214.

In at least one embodiment, SM 3214 comprises, without limitation, aprogrammable streaming processor that is configured to process tasksrepresented by a number of threads. In at least one embodiment, SM 3214is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently andimplements a Single-Instruction, Multiple-Data (“SIMD”) architecturewhere each thread in a group of threads (e.g., a warp) is configured toprocess a different set of data based on same set of instructions. In atleast one embodiment, all threads in group of threads execute a commonset of instructions. In at least one embodiment, SM 3214 implements aSingle-Instruction, Multiple Thread (“SIMT”) architecture wherein eachthread in a group of threads is configured to process a different set ofdata based on that common set of instructions, but where individualthreads in a group of threads are allowed to diverge during execution.In at least one embodiment, a program counter, call stack, and executionstate is maintained for each warp, enabling concurrency between warpsand serial execution within warps when threads within a warp diverge. Inanother embodiment, a program counter, call stack, and execution stateis maintained for each individual thread, enabling equal concurrencybetween all threads, within and between warps. In at least oneembodiment, execution state is maintained for each individual thread andthreads executing common instructions may be converged and executed inparallel for better efficiency. At least one embodiment of SM 3214 isdescribed in more detail herein.

In at least one embodiment, MMU 3218 provides an interface between GPC3200 and a memory partition unit (e.g., partition unit 3122 of FIG. 31)and MMU 3218 provides translation of virtual addresses into physicaladdresses, memory protection, and arbitration of memory requests. In atleast one embodiment, MMU 3218 provides one or more translationlookaside buffers (“TLBs”) for performing translation of virtualaddresses into physical addresses in memory.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, deep learning application processor is used to train amachine learning model, such as a neural network, to predict or inferinformation provided to GPC 3200. In at least one embodiment, GPC 3200is used to infer or predict information based on a trained machinelearning model (e.g., neural network) that has been trained by anotherprocessor or system or by GPC 3200. In at least one embodiment, GPC 3200may be used to perform one or more neural network use cases describedherein.

In at least one embodiment, one or more systems depicted in FIG. 32 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 32 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 32 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 33 illustrates a memory partition unit 3300 of a parallelprocessing unit (“PPU”), in accordance with at least one embodiment. Inat least one embodiment, memory partition unit 3300 includes, withoutlimitation, a Raster Operations (“ROP”) unit 3302, a level two (“L2”)cache 3304, a memory interface 3306, and any suitable combinationthereof. In at least one embodiment, memory interface 3306 is coupled tomemory. In at least one embodiment, memory interface 3306 may implement32, 64, 128, 1024-bit data buses, or like, for high-speed data transfer.In at least one embodiment, PPU incorporates U memory interfaces 3306where U is a positive integer, with one memory interface 3306 per pairof partition units 3300, where each pair of partition units 3300 isconnected to a corresponding memory device. For example, in at least oneembodiment, PPU may be connected to up to Y memory devices, such as highbandwidth memory stacks or graphics double-data-rate, version 5,synchronous dynamic random access memory (“GDDR5 SDRAM”).

In at least one embodiment, memory interface 3306 implements a highbandwidth memory second generation (“HBM2”) memory interface and Yequals half of U. In at least one embodiment, HBM2 memory stacks arelocated on a physical package with a PPU, providing substantial powerand area savings compared with conventional GDDR5 SDRAM systems. In atleast one embodiment, each HBM2 stack includes, without limitation, fourmemory dies with Y=4, with each HBM2 stack including two 128-bitchannels per die for a total of 8 channels and a data bus width of 1024bits. In at least one embodiment, that memory supports Single-ErrorCorrecting Double-Error Detecting (“SECDED”) Error Correction Code(“ECC”) to protect data. In at least one embodiment, ECC can providehigher reliability for compute applications that are sensitive to datacorruption.

In at least one embodiment, PPU implements a multi-level memoryhierarchy. In at least one embodiment, memory partition unit 3300supports a unified memory to provide a single unified virtual addressspace for central processing unit (“CPU”) and PPU memory, enabling datasharing between virtual memory systems. In at least one embodimentfrequency of accesses by a PPU to a memory located on other processorsis traced to ensure that memory pages are moved to physical memory ofPPU that is accessing pages more frequently. In at least one embodiment,high-speed GPU interconnect 3108 supports address translation servicesallowing PPU to directly access a CPU's page tables and providing fullaccess to CPU memory by a PPU.

In at least one embodiment, copy engines transfer data between multiplePPUs or between PPUs and CPUs. In at least one embodiment, copy enginescan generate page faults for addresses that are not mapped into pagetables and memory partition unit 3300 then services page faults, mappingaddresses into page table, after which copy engine performs a transfer.In at least one embodiment, memory is pinned (i.e., non-pageable) formultiple copy engine operations between multiple processors,substantially reducing available memory. In at least one embodiment,with hardware page faulting, addresses can be passed to copy engineswithout regard as to whether memory pages are resident, and a copyprocess is transparent.

Data from memory 3104 of FIG. 31 or other system memory is fetched bymemory partition unit 3300 and stored in L2 cache 3304, which is locatedon-chip and is shared between various GPCs, in accordance with at leastone embodiment. Each memory partition unit 3300, in at least oneembodiment, includes, without limitation, at least a portion of L2 cacheassociated with a corresponding memory device. In at least oneembodiment, lower level caches are implemented in various units withinGPCs. In at least one embodiment, each of SMs 3214 in FIG. 32 mayimplement a Level 1 (“L1”) cache wherein that L1 cache is private memorythat is dedicated to a particular SM 3214 and data from L2 cache 3304 isfetched and stored in each L1 cache for processing in functional unitsof SMs 3214. In at least one embodiment, L2 cache 3304 is coupled tomemory interface 3306 and XBar 3120 shown in FIG. 31.

ROP unit 3302 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and more, in at leastone embodiment. ROP unit 3302, in at least one embodiment, implementsdepth testing in conjunction with raster engine 3208, receiving a depthfor a sample location associated with a pixel fragment from a cullingengine of raster engine 3208. In at least one embodiment, depth istested against a corresponding depth in a depth buffer for a samplelocation associated with a fragment. In at least one embodiment, if thatfragment passes that depth test for that sample location, then ROP unit3302 updates depth buffer and transmits a result of that depth test toraster engine 3208. It will be appreciated that a number of partitionunits 3300 may be different than a number of GPCs and, therefore, eachROP unit 3302 can, in at least one embodiment, be coupled to each GPC.In at least one embodiment, ROP unit 3302 tracks packets received fromdifferent GPCs and determines whether a result generated by ROP unit3302 is to be routed to through XBar 3120.

In at least one embodiment, one or more systems depicted in FIG. 33 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 33 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 33 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 34 illustrates a streaming multi-processor (“SM”) 3400, accordingto at least one embodiment. In at least one embodiment, SM 3400 is SM ofFIG. 32. In at least one embodiment, SM 3400 includes, withoutlimitation, an instruction cache 3402, one or more scheduler units 3404,a register file 3408, one or more processing cores (“cores”) 3410, oneor more special function units (“SFUs”) 3412, one or more load/storeunits (“LSUs”) 3414, an interconnect network 3416, a shared memory/levelone (“L1”) cache 3418, and/or any suitable combination thereof.

In at least one embodiment, a work distribution unit dispatches tasksfor execution on general processing clusters (“GPCs”) of parallelprocessing units (“PPUs”) and each task is allocated to a particularData Processing Cluster (“DPC”) within a GPC and, if a task isassociated with a shader program, that task is allocated to one of SMs3400. In at least one embodiment, scheduler unit 3404 receives tasksfrom a work distribution unit and manages instruction scheduling for oneor more thread blocks assigned to SM 3400. In at least one embodiment,scheduler unit 3404 schedules thread blocks for execution as warps ofparallel threads, wherein each thread block is allocated at least onewarp. In at least one embodiment, each warp executes threads. In atleast one embodiment, scheduler unit 3404 manages a plurality ofdifferent thread blocks, allocating warps to different thread blocks andthen dispatching instructions from plurality of different cooperativegroups to various functional units (e.g., processing cores 3410, SFUs3412, and LSUs 3414) during each clock cycle.

In at least one embodiment, Cooperative Groups may refer to aprogramming model for organizing groups of communicating threads thatallows developers to express granularity at which threads arecommunicating, enabling expression of richer, more efficient paralleldecompositions. In at least one embodiment, cooperative launch APIssupport synchronization amongst thread blocks for execution of parallelalgorithms. In at least one embodiment, applications of conventionalprogramming models provide a single, simple construct for synchronizingcooperating threads: a barrier across all threads of a thread block(e.g., syncthreads( ) function). However, in at least one embodiment,programmers may define groups of threads at smaller than thread blockgranularities and synchronize within defined groups to enable greaterperformance, design flexibility, and software reuse in form ofcollective group-wide function interfaces. In at least one embodiment,Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (i.e., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on threads in a cooperative group. In at least oneembodiment, that programming model supports clean composition acrosssoftware boundaries, so that libraries and utility functions cansynchronize safely within their local context without having to makeassumptions about convergence. In at least one embodiment, CooperativeGroups primitives enable new patterns of cooperative parallelism,including, without limitation, producer-consumer parallelism,opportunistic parallelism, and global synchronization across an entiregrid of thread blocks.

In at least one embodiment, a dispatch unit 3406 is configured totransmit instructions to one or more functional units and scheduler unit3404 and includes, without limitation, two dispatch units 3406 thatenable two different instructions from a common warp to be dispatchedduring each clock cycle. In at least one embodiment, each scheduler unit3404 includes a single dispatch unit 3406 or additional dispatch units3406.

In at least one embodiment, each SM 3400, in at least one embodiment,includes, without limitation, register file 3408 that provides a set ofregisters for functional units of SM 3400. In at least one embodiment,register file 3408 is divided between each functional unit such thateach functional unit is allocated a dedicated portion of register file3408. In at least one embodiment, register file 3408 is divided betweendifferent warps being executed by SM 3400 and register file 3408provides temporary storage for operands connected to data paths offunctional units. In at least one embodiment, each SM 3400 comprises,without limitation, a plurality of L processing cores 3410, where L is apositive integer. In at least one embodiment, SM 3400 includes, withoutlimitation, a large number (e.g., 128 or more) of distinct processingcores 3410. In at least one embodiment, each processing core 3410includes, without limitation, a fully-pipelined, single-precision,double-precision, and/or mixed precision processing unit that includes,without limitation, a floating point arithmetic logic unit and aninteger arithmetic logic unit. In at least one embodiment, floatingpoint arithmetic logic units implement IEEE 754-2008 standard forfloating point arithmetic. In at least one embodiment, processing cores3410 include, without limitation, 64 single-precision (32-bit) floatingpoint cores, 64 integer cores, 32 double-precision (64-bit) floatingpoint cores, and 8 tensor cores.

Tensor cores are configured to perform matrix operations in accordancewith at least one embodiment. In at least one embodiment, one or moretensor cores are included in processing cores 3410. In at least oneembodiment, tensor cores are configured to perform deep learning matrixarithmetic, such as convolution operations for neural network trainingand inferencing. In at least one embodiment, each tensor core operateson a 4×4 matrix and performs a matrix multiply and accumulate operation,D=A×B+C, where A, B, C, and D are 4×4 matrices.

In at least one embodiment, matrix multiply inputs A and B are 16-bitfloating point matrices and accumulation matrices C and D are 16-bitfloating point or 32-bit floating point matrices. In at least oneembodiment, tensor cores operate on 16-bit floating point input datawith 32-bit floating point accumulation. In at least one embodiment,16-bit floating point multiply uses 64 operations and results in a fullprecision product that is then accumulated using 32-bit floating pointaddition with other intermediate products for a 4×4×4 matrix multiply.Tensor cores are used to perform much larger two-dimensional or higherdimensional matrix operations, built up from these smaller elements, inat least one embodiment. In at least one embodiment, an API, such as aCUDA 9 C++ API, exposes specialized matrix load, matrix multiply andaccumulate, and matrix store operations to efficiently use tensor coresfrom a CUDA-C++ program. In at least one embodiment, at a CUDA level, awarp-level interface assumes 16×16 size matrices spanning all 32 threadsof warp.

In at least one embodiment, each SM 3400 comprises, without limitation,M SFUs 3412 that perform special functions (e.g., attribute evaluation,reciprocal square root, and like). In at least one embodiment, SFUs 3412include, without limitation, a tree traversal unit configured totraverse a hierarchical tree data structure. In at least one embodiment,SFUs 3412 include, without limitation, a texture unit configured toperform texture map filtering operations. In at least one embodiment,texture units are configured to load texture maps (e.g., a 2D array oftexels) from memory and sample texture maps to produce sampled texturevalues for use in shader programs executed by SM 3400. In at least oneembodiment, texture maps are stored in shared memory/L1 cache 3418. Inat least one embodiment, texture units implement texture operations suchas filtering operations using mip-maps (e.g., texture maps of varyinglevels of detail), in accordance with at least one embodiment. In atleast one embodiment, each SM 3400 includes, without limitation, twotexture units.

Each SM 3400 comprises, without limitation, N LSUs 3414 that implementload and store operations between shared memory/L1 cache 3418 andregister file 3408, in at least one embodiment. Interconnect network3416 connects each functional unit to register file 3408 and LSU 3414 toregister file 3408 and shared memory/L1 cache 3418 in at least oneembodiment. In at least one embodiment, interconnect network 3416 is acrossbar that can be configured to connect any functional units to anyregisters in register file 3408 and connect LSUs 3414 to register file3408 and memory locations in shared memory/L1 cache 3418.

In at least one embodiment, shared memory/L1 cache 3418 is an array ofon-chip memory that allows for data storage and communication between SM3400 and primitive engine and between threads in SM 3400, in at leastone embodiment. In at least one embodiment, shared memory/L1 cache 3418comprises, without limitation, 128 KB of storage capacity and is in apath from SM 3400 to a partition unit. In at least one embodiment,shared memory/L1 cache 3418, in at least one embodiment, is used tocache reads and writes. In at least one embodiment, one or more ofshared memory/L1 cache 3418, L2 cache, and memory are backing stores.

Combining data cache and shared memory functionality into a singlememory block provides improved performance for both types of memoryaccesses, in at least one embodiment. In at least one embodiment,capacity is used or is usable as a cache by programs that do not useshared memory, such as if shared memory is configured to use half of acapacity, and texture and load/store operations can use remainingcapacity. Integration within shared memory/L1 cache 3418 enables sharedmemory/L1 cache 3418 to function as a high-throughput conduit forstreaming data while simultaneously providing high-bandwidth andlow-latency access to frequently reused data, in accordance with atleast one embodiment. In at least one embodiment, when configured forgeneral purpose parallel computation, a simpler configuration can beused compared with graphics processing. In at least one embodiment,fixed function graphics processing units are bypassed, creating a muchsimpler programming model. In a general purpose parallel computationconfiguration, a work distribution unit assigns and distributes blocksof threads directly to DPCs, in at least one embodiment. In at least oneembodiment, threads in a block execute a common program, using a uniquethread ID in calculation to ensure each thread generates unique results,using SM 3400 to execute program and perform calculations, sharedmemory/L1 cache 3418 to communicate between threads, and LSU 3414 toread and write global memory through shared memory/L1 cache 3418 andmemory partition unit. In at least one embodiment, when configured forgeneral purpose parallel computation, SM 3400 writes commands thatscheduler unit 3404 can use to launch new work on DPCs.

In at least one embodiment, a PPU is included in or coupled to a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (“PDA”), a digital camera, a vehicle, a head mounted display,a hand-held electronic device, and more. In at least one embodiment, aPPU is embodied on a single semiconductor substrate. In at least oneembodiment, a PPU is included in a system-on-a-chip (“SoC”) along withone or more other devices such as additional PPUs, memory, a reducedinstruction set computer (“RISC”) CPU, a memory management unit (“MMU”),a digital-to-analog converter (“DAC”), and like.

In at least one embodiment, a PPU may be included on a graphics cardthat includes one or more memory devices. In at least one embodiment,that graphics card may be configured to interface with a PCIe slot on amotherboard of a desktop computer. In at least one embodiment, that PPUmay be an integrated graphics processing unit (“iGPU”) included inchipset of a motherboard.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B. In at least oneembodiment, deep learning application processor is used to train amachine learning model, such as a neural network, to predict or inferinformation provided to SM 3400. In at least one embodiment, SM 3400 isused to infer or predict information based on a trained machine learningmodel (e.g., neural network) that has been trained by another processoror system or by SM 3400. In at least one embodiment, SM 3400 may be usedto perform one or more neural network use cases described herein.

In at least one embodiment, one or more systems depicted in FIG. 34 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 34 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 34 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

Embodiments are disclosed related a virtualized computing platform foradvanced computing, such as image inferencing and image processing inmedical applications. Without limitation, embodiments may includeradiography, magnetic resonance imaging (MRI), nuclear medicine,ultrasound, sonography, elastography, photoacoustic imaging, tomography,echocardiography, functional near-infrared spectroscopy, and magneticparticle imaging, or a combination thereof. In at least one embodiment,a virtualized computing platform and associated processes describedherein may additionally or alternatively be used, without limitation, inforensic science analysis, sub-surface detection and imaging (e.g., oilexploration, archaeology, paleontology, etc.), topography, oceanography,geology, osteology, meteorology, intelligent area or object tracking andmonitoring, sensor data processing (e.g., RADAR, SONAR, LIDAR, etc.),and/or genomics and gene sequencing.

With reference to FIG. 35, FIG. 35 is an example data flow diagram for aprocess 3500 of generating and deploying an image processing andinferencing pipeline, in accordance with at least one embodiment. In atleast one embodiment, process 3500 may be deployed for use with imagingdevices, processing devices, genomics devices, gene sequencing devices,radiology devices, and/or other device types at one or more facilities3502, such as medical facilities, hospitals, healthcare institutes,clinics, research or diagnostic labs, etc. In at least one embodiment,process 3500 may be deployed to perform genomics analysis andinferencing on sequencing data. Examples of genomic analyses that may beperformed using systems and processes described herein include, withoutlimitation, variant calling, mutation detection, and gene expressionquantification.

In at least one embodiment, process 3500 may be executed within atraining system 3504 and/or a deployment system 3506. In at least oneembodiment, training system 3504 may be used to perform training,deployment, and implementation of machine learning models (e.g., neuralnetworks, object detection algorithms, computer vision algorithms, etc.)for use in deployment system 3506. In at least one embodiment,deployment system 3506 may be configured to offload processing andcompute resources among a distributed computing environment to reduceinfrastructure requirements at facility 3502. In at least oneembodiment, deployment system 3506 may provide a streamlined platformfor selecting, customizing, and implementing virtual instruments for usewith imaging devices (e.g., MRI, CT Scan, X-Ray, Ultrasound, etc.) orsequencing devices at facility 3502. In at least one embodiment, virtualinstruments may include software-defined applications for performing oneor more processing operations with respect to imaging data generated byimaging devices, sequencing devices, radiology devices, and/or otherdevice types. In at least one embodiment, one or more applications in apipeline may use or call upon services (e.g., inference, visualization,compute, AI, etc.) of deployment system 3506 during execution ofapplications.

In at least one embodiment, some of applications used in advancedprocessing and inferencing pipelines may use machine learning models orother AI to perform one or more processing steps. In at least oneembodiment, machine learning models may be trained at facility 3502using data 3508 (such as imaging data) generated at facility 3502 (andstored on one or more picture archiving and communication system (PACS)servers at facility 3502), may be trained using imaging or sequencingdata 3508 from another facility or facilities (e.g., a differenthospital, lab, clinic, etc.), or a combination thereof. In at least oneembodiment, training system 3504 may be used to provide applications,services, and/or other resources for generating working, deployablemachine learning models for deployment system 3506.

In at least one embodiment, a model registry 3524 may be backed byobject storage that may support versioning and object metadata. In atleast one embodiment, object storage may be accessible through, forexample, a cloud storage (e.g., a cloud 3626 of FIG. 36) compatibleapplication programming interface (API) from within a cloud platform. Inat least one embodiment, machine learning models within model registry3524 may uploaded, listed, modified, or deleted by developers orpartners of a system interacting with an API. In at least oneembodiment, an API may provide access to methods that allow users withappropriate credentials to associate models with applications, such thatmodels may be executed as part of execution of containerizedinstantiations of applications.

In at least one embodiment, a training pipeline 3604 (FIG. 36) mayinclude a scenario where facility 3502 is training their own machinelearning model, or has an existing machine learning model that needs tobe optimized or updated. In at least one embodiment, imaging data 3508generated by imaging device(s), sequencing devices, and/or other devicetypes may be received. In at least one embodiment, once imaging data3508 is received, AI-assisted annotation 3510 may be used to aid ingenerating annotations corresponding to imaging data 3508 to be used asground truth data for a machine learning model. In at least oneembodiment, AI-assisted annotation 3510 may include one or more machinelearning models (e.g., convolutional neural networks (CNNs)) that may betrained to generate annotations corresponding to certain types ofimaging data 3508 (e.g., from certain devices) and/or certain types ofanomalies in imaging data 3508. In at least one embodiment, AI-assistedannotations 3510 may then be used directly, or may be adjusted orfine-tuned using an annotation tool (e.g., by a researcher, a clinician,a doctor, a scientist, etc.), to generate ground truth data. In at leastone embodiment, in some examples, labeled clinic data 3512 (e.g.,annotations provided by a clinician, doctor, scientist, technician,etc.) may be used as ground truth data for training a machine learningmodel. In at least one embodiment, AI-assisted annotations 3510, labeledclinic data 3512, or a combination thereof may be used as ground truthdata for training a machine learning model. In at least one embodiment,a trained machine learning model may be referred to as an output model3516, and may be used by deployment system 3506, as described herein.

In at least one embodiment, training pipeline 3604 (FIG. 36) may includea scenario where facility 3502 needs a machine learning model for use inperforming one or more processing tasks for one or more applications indeployment system 3506, but facility 3502 may not currently have such amachine learning model (or may not have a model that is optimized,efficient, or effective for such purposes). In at least one embodiment,an existing machine learning model may be selected from model registry3524. In at least one embodiment, model registry 3524 may includemachine learning models trained to perform a variety of differentinference tasks on imaging data. In at least one embodiment, machinelearning models in model registry 3524 may have been trained on imagingdata from different facilities than facility 3502 (e.g., facilitiesremotely located). In at least one embodiment, machine learning modelsmay have been trained on imaging data from one location, two locations,or any number of locations. In at least one embodiment, when beingtrained on imaging data from a specific location, training may takeplace at that location, or at least in a manner that protectsconfidentiality of imaging data or restricts imaging data from beingtransferred off-premises (e.g., to comply with HIPAA regulations,privacy regulations, etc.). In at least one embodiment, once a model istrained—or partially trained—at one location, a machine learning modelmay be added to model registry 3524. In at least one embodiment, amachine learning model may then be retrained, or updated, at any numberof other facilities, and a retrained or updated model may be madeavailable in model registry 3524. In at least one embodiment, a machinelearning model may then be selected from model registry 3524—andreferred to as output model 3516—and may be used in deployment system3506 to perform one or more processing tasks for one or moreapplications of a deployment system.

In at least one embodiment, training pipeline 3604 (FIG. 36) may be usedin a scenario that includes facility 3502 requiring a machine learningmodel for use in performing one or more processing tasks for one or moreapplications in deployment system 3506, but facility 3502 may notcurrently have such a machine learning model (or may not have a modelthat is optimized, efficient, or effective for such purposes). In atleast one embodiment, a machine learning model selected from modelregistry 3524 might not be fine-tuned or optimized for imaging data 3508generated at facility 3502 because of differences in populations,genetic variations, robustness of training data used to train a machinelearning model, diversity in anomalies of training data, and/or otherissues with training data. In at least one embodiment, AI-assistedannotation 3510 may be used to aid in generating annotationscorresponding to imaging data 3508 to be used as ground truth data forretraining or updating a machine learning model. In at least oneembodiment, labeled clinic data 3512 (e.g., annotations provided by aclinician, doctor, scientist, etc.) may be used as ground truth data fortraining a machine learning model. In at least one embodiment,retraining or updating a machine learning model may be referred to asmodel training 3514. In at least one embodiment, model training3514—e.g., AI-assisted annotations 3510, labeled clinic data 3512, or acombination thereof—may be used as ground truth data for retraining orupdating a machine learning model.

In at least one embodiment, deployment system 3506 may include software3518, services 3520, hardware 3522, and/or other components, features,and functionality. In at least one embodiment, deployment system 3506may include a software “stack,” such that software 3518 may be built ontop of services 3520 and may use services 3520 to perform some or all ofprocessing tasks, and services 3520 and software 3518 may be built ontop of hardware 3522 and use hardware 3522 to execute processing,storage, and/or other compute tasks of deployment system 3506.

In at least one embodiment, software 3518 may include any number ofdifferent containers, where each container may execute an instantiationof an application. In at least one embodiment, each application mayperform one or more processing tasks in an advanced processing andinferencing pipeline (e.g., inferencing, object detection, featuredetection, segmentation, image enhancement, calibration, etc.). In atleast one embodiment, for each type of imaging device (e.g., CT, MRI,X-Ray, ultrasound, sonography, echocardiography, etc.), sequencingdevice, radiology device, genomics device, etc., there may be any numberof containers that may perform a data processing task with respect toimaging data 3508 (or other data types, such as those described herein)generated by a device. In at least one embodiment, an advancedprocessing and inferencing pipeline may be defined based on selectionsof different containers that are desired or required for processingimaging data 3508, in addition to containers that receive and configureimaging data for use by each container and/or for use by facility 3502after processing through a pipeline (e.g., to convert outputs back to ausable data type, such as digital imaging and communications in medicine(DICOM) data, radiology information system (RIS) data, clinicalinformation system (CIS) data, remote procedure call (RPC) data, datasubstantially compliant with a representation state transfer (REST)interface, data substantially compliant with a file-based interface,and/or raw data, for storage and display at facility 3502). In at leastone embodiment, a combination of containers within software 3518 (e.g.,that make up a pipeline) may be referred to as a virtual instrument (asdescribed in more detail herein), and a virtual instrument may leverageservices 3520 and hardware 3522 to execute some or all processing tasksof applications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive inputdata (e.g., imaging data 3508) in a DICOM, RIS, CIS, REST compliant,RPC, raw, and/or other format in response to an inference request (e.g.,a request from a user of deployment system 3506, such as a clinician, adoctor, a radiologist, etc.). In at least one embodiment, input data maybe representative of one or more images, video, and/or other datarepresentations generated by one or more imaging devices, sequencingdevices, radiology devices, genomics devices, and/or other device types.In at least one embodiment, data may undergo pre-processing as part ofdata processing pipeline to prepare data for processing by one or moreapplications. In at least one embodiment, post-processing may beperformed on an output of one or more inferencing tasks or otherprocessing tasks of a pipeline to prepare an output data for a nextapplication and/or to prepare output data for transmission and/or use bya user (e.g., as a response to an inference request). In at least oneembodiment, inferencing tasks may be performed by one or more machinelearning models, such as trained or deployed neural networks, which mayinclude output models 3516 of training system 3504.

In at least one embodiment, tasks of data processing pipeline may beencapsulated in a container(s) that each represent a discrete, fullyfunctional instantiation of an application and virtualized computingenvironment that is able to reference machine learning models. In atleast one embodiment, containers or applications may be published into aprivate (e.g., limited access) area of a container registry (describedin more detail herein), and trained or deployed models may be stored inmodel registry 3524 and associated with one or more applications. In atleast one embodiment, images of applications (e.g., container images)may be available in a container registry, and once selected by a userfrom a container registry for deployment in a pipeline, an image may beused to generate a container for an instantiation of an application foruse by a user's system.

In at least one embodiment, developers (e.g., software developers,clinicians, doctors, etc.) may develop, publish, and store applications(e.g., as containers) for performing image processing and/or inferencingon supplied data. In at least one embodiment, development, publishing,and/or storing may be performed using a software development kit (SDK)associated with a system (e.g., to ensure that an application and/orcontainer developed is compliant with or compatible with a system). Inat least one embodiment, an application that is developed may be testedlocally (e.g., at a first facility, on data from a first facility) withan SDK which may support at least some of services 3520 as a system(e.g., system 3600 of FIG. 36). In at least one embodiment, becauseDICOM objects may contain anywhere from one to hundreds of images orother data types, and due to a variation in data, a developer may beresponsible for managing (e.g., setting constructs for, buildingpre-processing into an application, etc.) extraction and preparation ofincoming DICOM data. In at least one embodiment, once validated bysystem 3600 (e.g., for accuracy, safety, patient privacy, etc.), anapplication may be available in a container registry for selectionand/or implementation by a user (e.g., a hospital, clinic, lab,healthcare provider, etc.) to perform one or more processing tasks withrespect to data at a facility (e.g., a second facility) of a user.

In at least one embodiment, developers may then share applications orcontainers through a network for access and use by users of a system(e.g., system 3600 of FIG. 36). In at least one embodiment, completedand validated applications or containers may be stored in a containerregistry and associated machine learning models may be stored in modelregistry 3524. In at least one embodiment, a requesting entity (e.g., auser at a medical facility)—who provides an inference or imageprocessing request—may browse a container registry and/or model registry3524 for an application, container, dataset, machine learning model,etc., select a desired combination of elements for inclusion in dataprocessing pipeline, and submit an imaging processing request. In atleast one embodiment, a request may include input data (and associatedpatient data, in some examples) that is necessary to perform a request,and/or may include a selection of application(s) and/or machine learningmodels to be executed in processing a request. In at least oneembodiment, a request may then be passed to one or more components ofdeployment system 3506 (e.g., a cloud) to perform processing of dataprocessing pipeline. In at least one embodiment, processing bydeployment system 3506 may include referencing selected elements (e.g.,applications, containers, models, etc.) from a container registry and/ormodel registry 3524. In at least one embodiment, once results aregenerated by a pipeline, results may be returned to a user for reference(e.g., for viewing in a viewing application suite executing on a local,on-premises workstation or terminal). In at least one embodiment, aradiologist may receive results from an data processing pipelineincluding any number of application and/or containers, where results mayinclude anomaly detection in X-rays, CT scans, MRIs, etc.

In at least one embodiment, to aid in processing or execution ofapplications or containers in pipelines, services 3520 may be leveraged.In at least one embodiment, services 3520 may include compute services,artificial intelligence (AI) services, visualization services, and/orother service types. In at least one embodiment, services 3520 mayprovide functionality that is common to one or more applications insoftware 3518, so functionality may be abstracted to a service that maybe called upon or leveraged by applications. In at least one embodiment,functionality provided by services 3520 may run dynamically and moreefficiently, while also scaling well by allowing applications to processdata in parallel (e.g., using a parallel computing platform 3630 (FIG.36)). In at least one embodiment, rather than each application thatshares a same functionality offered by a service 3520 being required tohave a respective instance of service 3520, service 3520 may be sharedbetween and among various applications. In at least one embodiment,services may include an inference server or engine that may be used forexecuting detection or segmentation tasks, as non-limiting examples. Inat least one embodiment, a model training service may be included thatmay provide machine learning model training and/or retrainingcapabilities. In at least one embodiment, a data augmentation servicemay further be included that may provide GPU accelerated data (e.g.,DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing,scaling, and/or other augmentation. In at least one embodiment, avisualization service may be used that may add image renderingeffects—such as ray-tracing, rasterization, denoising, sharpening,etc.—to add realism to two-dimensional (2D) and/or three-dimensional(3D) models. In at least one embodiment, virtual instrument services maybe included that provide for beam-forming, segmentation, inferencing,imaging, and/or support for other applications within pipelines ofvirtual instruments.

In at least one embodiment, where a service 3520 includes an AI service(e.g., an inference service), one or more machine learning modelsassociated with an application for anomaly detection (e.g., tumors,growth abnormalities, scarring, etc.) may be executed by calling upon(e.g., as an API call) an inference service (e.g., an inference server)to execute machine learning model(s), or processing thereof, as part ofapplication execution. In at least one embodiment, where anotherapplication includes one or more machine learning models forsegmentation tasks, an application may call upon an inference service toexecute machine learning models for performing one or more of processingoperations associated with segmentation tasks. In at least oneembodiment, software 3518 implementing advanced processing andinferencing pipeline that includes segmentation application and anomalydetection application may be streamlined because each application maycall upon a same inference service to perform one or more inferencingtasks.

In at least one embodiment, hardware 3522 may include GPUs, CPUs,graphics cards, an AI/deep learning system (e.g., an AI supercomputer,such as NVIDIA's DGX supercomputer system), a cloud platform, or acombination thereof. In at least one embodiment, different types ofhardware 3522 may be used to provide efficient, purpose-built supportfor software 3518 and services 3520 in deployment system 3506. In atleast one embodiment, use of GPU processing may be implemented forprocessing locally (e.g., at facility 3502), within an AI/deep learningsystem, in a cloud system, and/or in other processing components ofdeployment system 3506 to improve efficiency, accuracy, and efficacy ofimage processing, image reconstruction, segmentation, MRI exams, strokeor heart attack detection (e.g., in real-time), image quality inrendering, etc. In at least one embodiment, a facility may includeimaging devices, genomics devices, sequencing devices, and/or otherdevice types on-premises that may leverage GPUs to generate imaging datarepresentative of a subject's anatomy.

In at least one embodiment, software 3518 and/or services 3520 may beoptimized for GPU processing with respect to deep learning, machinelearning, and/or high-performance computing, as non-limiting examples.In at least one embodiment, at least some of computing environment ofdeployment system 3506 and/or training system 3504 may be executed in adatacenter one or more supercomputers or high performance computingsystems, with GPU optimized software (e.g., hardware and softwarecombination of NVIDIA's DGX system). In at least one embodiment,datacenters may be compliant with provisions of HIPAA, such thatreceipt, processing, and transmission of imaging data and/or otherpatient data is securely handled with respect to privacy of patientdata. In at least one embodiment, hardware 3522 may include any numberof GPUs that may be called upon to perform processing of data inparallel, as described herein. In at least one embodiment, cloudplatform may further include GPU processing for GPU-optimized executionof deep learning tasks, machine learning tasks, or other computingtasks. In at least one embodiment, cloud platform (e.g., NVIDIA's NGC)may be executed using an AI/deep learning supercomputer(s) and/orGPU-optimized software (e.g., as provided on NVIDIA's DGX systems) as ahardware abstraction and scaling platform. In at least one embodiment,cloud platform may integrate an application container clustering systemor orchestration system (e.g., KUBERNETES) on multiple GPUs to enableseamless scaling and load balancing.

In at least one embodiment, one or more systems depicted in FIG. 35 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 35 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 35 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 36 is a system diagram for an example system 3600 for generatingand deploying an imaging deployment pipeline, in accordance with atleast one embodiment. In at least one embodiment, system 3600 may beused to implement process 3500 of FIG. 35 and/or other processesincluding advanced processing and inferencing pipelines. In at least oneembodiment, system 3600 may include training system 3504 and deploymentsystem 3506. In at least one embodiment, training system 3504 anddeployment system 3506 may be implemented using software 3518, services3520, and/or hardware 3522, as described herein.

In at least one embodiment, system 3600 (e.g., training system 3504and/or deployment system 3506) may implemented in a cloud computingenvironment (e.g., using cloud 3626). In at least one embodiment, system3600 may be implemented locally with respect to a healthcare servicesfacility, or as a combination of both cloud and local computingresources. In at least one embodiment, in embodiments where cloudcomputing is implemented, patient data may be separated from, orunprocessed by, by one or more components of system 3600 that wouldrender processing non-compliant with HIPAA and/or other data handlingand privacy regulations or laws. In at least one embodiment, access toAPIs in cloud 3626 may be restricted to authorized users through enactedsecurity measures or protocols. In at least one embodiment, a securityprotocol may include web tokens that may be signed by an authentication(e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriateauthorization. In at least one embodiment, APIs of virtual instruments(described herein), or other instantiations of system 3600, may berestricted to a set of public IPs that have been vetted or authorizedfor interaction.

In at least one embodiment, various components of system 3600 maycommunicate between and among one another using any of a variety ofdifferent network types, including but not limited to local areanetworks (LANs) and/or wide area networks (WANs) via wired and/orwireless communication protocols. In at least one embodiment,communication between facilities and components of system 3600 (e.g.,for transmitting inference requests, for receiving results of inferencerequests, etc.) may be communicated over a data bus or data busses,wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet),etc.

In at least one embodiment, training system 3504 may execute trainingpipelines 3604, similar to those described herein with respect to FIG.35. In at least one embodiment, where one or more machine learningmodels are to be used in deployment pipelines 3610 by deployment system3506, training pipelines 3604 may be used to train or retrain one ormore (e.g., pre-trained) models, and/or implement one or more ofpre-trained models 3606 (e.g., without a need for retraining orupdating). In at least one embodiment, as a result of training pipelines3604, output model(s) 3516 may be generated. In at least one embodiment,training pipelines 3604 may include any number of processing steps, suchas but not limited to imaging data (or other input data) conversion oradaption (e.g., using DICOM adapter 3602A to convert DICOM images toanother format suitable for processing by respective machine learningmodels, such as Neuroimaging Informatics Technology Initiative (NIfTI)format), AI-assisted annotation 3510, labeling or annotating of imagingdata 3508 to generate labeled clinic data 3512, model selection from amodel registry, model training 3514, training, retraining, or updatingmodels, and/or other processing steps. In at least one embodiment, fordifferent machine learning models used by deployment system 3506,different training pipelines 3604 may be used. In at least oneembodiment, training pipeline 3604 similar to a first example describedwith respect to FIG. 35 may be used for a first machine learning model,training pipeline 3604 similar to a second example described withrespect to FIG. 35 may be used for a second machine learning model, andtraining pipeline 3604 similar to a third example described with respectto FIG. 35 may be used for a third machine learning model. In at leastone embodiment, any combination of tasks within training system 3504 maybe used depending on what is required for each respective machinelearning model. In at least one embodiment, one or more of machinelearning models may already be trained and ready for deployment somachine learning models may not undergo any processing by trainingsystem 3504, and may be implemented by deployment system 3506.

In at least one embodiment, output model(s) 3516 and/or pre-trainedmodel(s) 3606 may include any types of machine learning models dependingon implementation or embodiment. In at least one embodiment, and withoutlimitation, machine learning models used by system 3600 may includemachine learning model(s) using linear regression, logistic regression,decision trees, support vector machines (SVM), Naïve Bayes, k-nearestneighbor (Knn), K means clustering, random forest, dimensionalityreduction algorithms, gradient boosting algorithms, neural networks(e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/ShortTerm Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In at least one embodiment, training pipelines 3604 may includeAI-assisted annotation, as described in more detail herein with respectto at least FIG. 39B. In at least one embodiment, labeled clinic data3512 (e.g., traditional annotation) may be generated by any number oftechniques. In at least one embodiment, labels or other annotations maybe generated within a drawing program (e.g., an annotation program), acomputer aided design (CAD) program, a labeling program, another type ofprogram suitable for generating annotations or labels for ground truth,and/or may be hand drawn, in some examples. In at least one embodiment,ground truth data may be synthetically produced (e.g., generated fromcomputer models or renderings), real produced (e.g., designed andproduced from real-world data), machine-automated (e.g., using featureanalysis and learning to extract features from data and then generatelabels), human annotated (e.g., labeler, or annotation expert, defineslocation of labels), and/or a combination thereof. In at least oneembodiment, for each instance of imaging data 3508 (or other data typeused by machine learning models), there may be corresponding groundtruth data generated by training system 3504. In at least oneembodiment, AI-assisted annotation may be performed as part ofdeployment pipelines 3610; either in addition to, or in lieu ofAI-assisted annotation included in training pipelines 3604. In at leastone embodiment, system 3600 may include a multi-layer platform that mayinclude a software layer (e.g., software 3518) of diagnosticapplications (or other application types) that may perform one or moremedical imaging and diagnostic functions. In at least one embodiment,system 3600 may be communicatively coupled to (e.g., via encryptedlinks) PACS server networks of one or more facilities. In at least oneembodiment, system 3600 may be configured to access and referenced data(e.g., DICOM data, RIS data, raw data, CIS data, REST compliant data,RPC data, raw data, etc.) from PACS servers (e.g., via a DICOM adapter3602, or another data type adapter such as RIS, CIS, REST compliant,RPC, raw, etc.) to perform operations, such as training machine learningmodels, deploying machine learning models, image processing,inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as asecure, encrypted, and/or authenticated API through which applicationsor containers may be invoked (e.g., called) from an externalenvironment(s) (e.g., facility 3502). In at least one embodiment,applications may then call or execute one or more services 3520 forperforming compute, AI, or visualization tasks associated withrespective applications, and software 3518 and/or services 3520 mayleverage hardware 3522 to perform processing tasks in an effective andefficient manner.

In at least one embodiment, deployment system 3506 may executedeployment pipelines 3610. In at least one embodiment, deploymentpipelines 3610 may include any number of applications that may besequentially, non-sequentially, or otherwise applied to imaging data(and/or other data types) generated by imaging devices, sequencingdevices, genomics devices, etc.—including AI-assisted annotation, asdescribed above. In at least one embodiment, as described herein, adeployment pipeline 3610 for an individual device may be referred to asa virtual instrument for a device (e.g., a virtual ultrasoundinstrument, a virtual CT scan instrument, a virtual sequencinginstrument, etc.). In at least one embodiment, for a single device,there may be more than one deployment pipeline 3610 depending oninformation desired from data generated by a device. In at least oneembodiment, where detections of anomalies are desired from an MRImachine, there may be a first deployment pipeline 3610, and where imageenhancement is desired from output of an Mill machine, there may be asecond deployment pipeline 3610.

In at least one embodiment, applications available for deploymentpipelines 3610 may include any application that may be used forperforming processing tasks on imaging data or other data from devices.In at least one embodiment, different applications may be responsiblefor image enhancement, segmentation, reconstruction, anomaly detection,object detection, feature detection, treatment planning, dosimetry, beamplanning (or other radiation treatment procedures), and/or otheranalysis, image processing, or inferencing tasks. In at least oneembodiment, deployment system 3506 may define constructs for each ofapplications, such that users of deployment system 3506 (e.g., medicalfacilities, labs, clinics, etc.) may understand constructs and adaptapplications for implementation within their respective facility. In atleast one embodiment, an application for image reconstruction may beselected for inclusion in deployment pipeline 3610, but data typegenerated by an imaging device may be different from a data type usedwithin an application. In at least one embodiment, DICOM adapter 3602B(and/or a DICOM reader) or another data type adapter or reader (e.g.,RIS, CIS, REST compliant, RPC, raw, etc.) may be used within deploymentpipeline 3610 to convert data to a form useable by an application withindeployment system 3506. In at least one embodiment, access to DICOM,RIS, CIS, REST compliant, RPC, raw, and/or other data type libraries maybe accumulated and pre-processed, including decoding, extracting, and/orperforming any convolutions, color corrections, sharpness, gamma, and/orother augmentations to data. In at least one embodiment, DICOM, RIS,CIS, REST compliant, RPC, and/or raw data may be unordered and apre-pass may be executed to organize or sort collected data. In at leastone embodiment, because various applications may share common imageoperations, in some embodiments, a data augmentation library (e.g., asone of services 3520) may be used to accelerate these operations. In atleast one embodiment, to avoid bottlenecks of conventional processingapproaches that rely on CPU processing, parallel computing platform 3630may be used for GPU acceleration of these processing tasks.

In at least one embodiment, an image reconstruction application mayinclude a processing task that includes use of a machine learning model.In at least one embodiment, a user may desire to use their own machinelearning model, or to select a machine learning model from modelregistry 3524. In at least one embodiment, a user may implement theirown machine learning model or select a machine learning model forinclusion in an application for performing a processing task. In atleast one embodiment, applications may be selectable and customizable,and by defining constructs of applications, deployment andimplementation of applications for a particular user are presented as amore seamless user experience. In at least one embodiment, by leveragingother features of system 3600—such as services 3520 and hardware3522—deployment pipelines 3610 may be even more user friendly, providefor easier integration, and produce more accurate, efficient, and timelyresults.

In at least one embodiment, deployment system 3506 may include a userinterface 3614 (e.g., a graphical user interface, a web interface, etc.)that may be used to select applications for inclusion in deploymentpipeline(s) 3610, arrange applications, modify or change applications orparameters or constructs thereof, use and interact with deploymentpipeline(s) 3610 during set-up and/or deployment, and/or to otherwiseinteract with deployment system 3506. In at least one embodiment,although not illustrated with respect to training system 3504, userinterface 3614 (or a different user interface) may be used for selectingmodels for use in deployment system 3506, for selecting models fortraining, or retraining, in training system 3504, and/or for otherwiseinteracting with training system 3504.

In at least one embodiment, pipeline manager 3612 may be used, inaddition to an application orchestration system 3628, to manageinteraction between applications or containers of deployment pipeline(s)3610 and services 3520 and/or hardware 3522. In at least one embodiment,pipeline manager 3612 may be configured to facilitate interactions fromapplication to application, from application to service 3520, and/orfrom application or service to hardware 3522. In at least oneembodiment, although illustrated as included in software 3518, this isnot intended to be limiting, and in some examples (e.g., as illustratedin FIG. 37) pipeline manager 3612 may be included in services 3520. Inat least one embodiment, application orchestration system 3628 (e.g.,Kubernetes, DOCKER, etc.) may include a container orchestration systemthat may group applications into containers as logical units forcoordination, management, scaling, and deployment. In at least oneembodiment, by associating applications from deployment pipeline(s) 3610(e.g., a reconstruction application, a segmentation application, etc.)with individual containers, each application may execute in aself-contained environment (e.g., at a kernel level) to increase speedand efficiency.

In at least one embodiment, each application and/or container (or imagethereof) may be individually developed, modified, and deployed (e.g., afirst user or developer may develop, modify, and deploy a firstapplication and a second user or developer may develop, modify, anddeploy a second application separate from a first user or developer),which may allow for focus on, and attention to, a task of a singleapplication and/or container(s) without being hindered by tasks ofanother application(s) or container(s). In at least one embodiment,communication, and cooperation between different containers orapplications may be aided by pipeline manager 3612 and applicationorchestration system 3628. In at least one embodiment, so long as anexpected input and/or output of each container or application is knownby a system (e.g., based on constructs of applications or containers),application orchestration system 3628 and/or pipeline manager 3612 mayfacilitate communication among and between, and sharing of resourcesamong and between, each of applications or containers. In at least oneembodiment, because one or more of applications or containers indeployment pipeline(s) 3610 may share same services and resources,application orchestration system 3628 may orchestrate, load balance, anddetermine sharing of services or resources between and among variousapplications or containers. In at least one embodiment, a scheduler maybe used to track resource requirements of applications or containers,current usage or planned usage of these resources, and resourceavailability. In at least one embodiment, a scheduler may thus allocateresources to different applications and distribute resources between andamong applications in view of requirements and availability of a system.In some examples, a scheduler (and/or other component of applicationorchestration system 3628) may determine resource availability anddistribution based on constraints imposed on a system (e.g., userconstraints), such as quality of service (QoS), urgency of need for dataoutputs (e.g., to determine whether to execute real-time processing ordelayed processing), etc.

In at least one embodiment, services 3520 leveraged by and shared byapplications or containers in deployment system 3506 may include computeservices 3616, AI services 3618, visualization services 3620, and/orother service types. In at least one embodiment, applications may call(e.g., execute) one or more of services 3520 to perform processingoperations for an application. In at least one embodiment, computeservices 3616 may be leveraged by applications to performsuper-computing or other high-performance computing (HPC) tasks. In atleast one embodiment, compute service(s) 3616 may be leveraged toperform parallel processing (e.g., using a parallel computing platform3630) for processing data through one or more of applications and/or oneor more tasks of a single application, substantially simultaneously. Inat least one embodiment, parallel computing platform 3630 (e.g.,NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU)(e.g., GPUs 3622). In at least one embodiment, a software layer ofparallel computing platform 3630 may provide access to virtualinstruction sets and parallel computational elements of GPUs, forexecution of compute kernels. In at least one embodiment, parallelcomputing platform 3630 may include memory and, in some embodiments, amemory may be shared between and among multiple containers, and/orbetween and among different processing tasks within a single container.In at least one embodiment, inter-process communication (IPC) calls maybe generated for multiple containers and/or for multiple processeswithin a container to use same data from a shared segment of memory ofparallel computing platform 3630 (e.g., where multiple different stagesof an application or multiple applications are processing sameinformation). In at least one embodiment, rather than making a copy ofdata and moving data to different locations in memory (e.g., aread/write operation), same data in same location of a memory may beused for any number of processing tasks (e.g., at a same time, atdifferent times, etc.). In at least one embodiment, as data is used togenerate new data as a result of processing, this information of a newlocation of data may be stored and shared between various applications.In at least one embodiment, location of data and a location of updatedor modified data may be part of a definition of how a payload isunderstood within containers.

In at least one embodiment, AI services 3618 may be leveraged to performinferencing services for executing machine learning model(s) associatedwith applications (e.g., tasked with performing one or more processingtasks of an application). In at least one embodiment, AI services 3618may leverage AI system 3624 to execute machine learning model(s) (e.g.,neural networks, such as CNNs) for segmentation, reconstruction, objectdetection, feature detection, classification, and/or other inferencingtasks. In at least one embodiment, applications of deploymentpipeline(s) 3610 may use one or more of output models 3516 from trainingsystem 3504 and/or other models of applications to perform inference onimaging data (e.g., DICOM data, RIS data, CIS data, REST compliant data,RPC data, raw data, etc.). In at least one embodiment, two or moreexamples of inferencing using application orchestration system 3628(e.g., a scheduler) may be available. In at least one embodiment, afirst category may include a high priority/low latency path that mayachieve higher service level agreements, such as for performinginference on urgent requests during an emergency, or for a radiologistduring diagnosis. In at least one embodiment, a second category mayinclude a standard priority path that may be used for requests that maybe non-urgent or where analysis may be performed at a later time. In atleast one embodiment, application orchestration system 3628 maydistribute resources (e.g., services 3520 and/or hardware 3522) based onpriority paths for different inferencing tasks of AI services 3618.

In at least one embodiment, shared storage may be mounted to AI services3618 within system 3600. In at least one embodiment, shared storage mayoperate as a cache (or other storage device type) and may be used toprocess inference requests from applications. In at least oneembodiment, when an inference request is submitted, a request may bereceived by a set of API instances of deployment system 3506, and one ormore instances may be selected (e.g., for best fit, for load balancing,etc.) to process a request. In at least one embodiment, to process arequest, a request may be entered into a database, a machine learningmodel may be located from model registry 3524 if not already in a cache,a validation step may ensure appropriate machine learning model isloaded into a cache (e.g., shared storage), and/or a copy of a model maybe saved to a cache. In at least one embodiment, a scheduler (e.g., ofpipeline manager 3612) may be used to launch an application that isreferenced in a request if an application is not already running or ifthere are not enough instances of an application. In at least oneembodiment, if an inference server is not already launched to execute amodel, an inference server may be launched. In at least one embodiment,any number of inference servers may be launched per model. In at leastone embodiment, in a pull model, in which inference servers areclustered, models may be cached whenever load balancing is advantageous.In at least one embodiment, inference servers may be statically loadedin corresponding, distributed servers.

In at least one embodiment, inferencing may be performed using aninference server that runs in a container. In at least one embodiment,an instance of an inference server may be associated with a model (andoptionally a plurality of versions of a model). In at least oneembodiment, if an instance of an inference server does not exist when arequest to perform inference on a model is received, a new instance maybe loaded. In at least one embodiment, when starting an inferenceserver, a model may be passed to an inference server such that a samecontainer may be used to serve different models so long as inferenceserver is running as a different instance.

In at least one embodiment, during application execution, an inferencerequest for a given application may be received, and a container (e.g.,hosting an instance of an inference server) may be loaded (if notalready), and a start procedure may be called. In at least oneembodiment, pre-processing logic in a container may load, decode, and/orperform any additional pre-processing on incoming data (e.g., using aCPU(s) and/or GPU(s)). In at least one embodiment, once data is preparedfor inference, a container may perform inference as necessary on data.In at least one embodiment, this may include a single inference call onone image (e.g., a hand X-ray), or may require inference on hundreds ofimages (e.g., a chest CT). In at least one embodiment, an applicationmay summarize results before completing, which may include, withoutlimitation, a single confidence score, pixel level-segmentation,voxel-level segmentation, generating a visualization, or generating textto summarize findings. In at least one embodiment, different models orapplications may be assigned different priorities. For example, somemodels may have a real-time (TAT less than one minute) priority whileothers may have lower priority (e.g., TAT less than 10 minutes). In atleast one embodiment, model execution times may be measured fromrequesting institution or entity and may include partner networktraversal time, as well as execution on an inference service.

In at least one embodiment, transfer of requests between services 3520and inference applications may be hidden behind a software developmentkit (SDK), and robust transport may be provide through a queue. In atleast one embodiment, a request will be placed in a queue via an API foran individual application/tenant ID combination and an SDK will pull arequest from a queue and give a request to an application. In at leastone embodiment, a name of a queue may be provided in an environment fromwhere an SDK will pick it up. In at least one embodiment, asynchronouscommunication through a queue may be useful as it may allow any instanceof an application to pick up work as it becomes available. In at leastone embodiment, results may be transferred back through a queue, toensure no data is lost. In at least one embodiment, queues may alsoprovide an ability to segment work, as highest priority work may go to aqueue with most instances of an application connected to it, whilelowest priority work may go to a queue with a single instance connectedto it that processes tasks in an order received. In at least oneembodiment, an application may run on a GPU-accelerated instancegenerated in cloud 3626, and an inference service may performinferencing on a GPU.

In at least one embodiment, visualization services 3620 may be leveragedto generate visualizations for viewing outputs of applications and/ordeployment pipeline(s) 3610. In at least one embodiment, GPUs 3622 maybe leveraged by visualization services 3620 to generate visualizations.In at least one embodiment, rendering effects, such as ray-tracing, maybe implemented by visualization services 3620 to generate higher qualityvisualizations. In at least one embodiment, visualizations may include,without limitation, 2D image renderings, 3D volume renderings, 3D volumereconstruction, 2D tomographic slices, virtual reality displays,augmented reality displays, etc. In at least one embodiment, virtualizedenvironments may be used to generate a virtual interactive display orenvironment (e.g., a virtual environment) for interaction by users of asystem (e.g., doctors, nurses, radiologists, etc.). In at least oneembodiment, visualization services 3620 may include an internalvisualizer, cinematics, and/or other rendering or image processingcapabilities or functionality (e.g., ray tracing, rasterization,internal optics, etc.).

In at least one embodiment, hardware 3522 may include GPUs 3622, AIsystem 3624, cloud 3626, and/or any other hardware used for executingtraining system 3504 and/or deployment system 3506. In at least oneembodiment, GPUs 3622 (e.g., NVIDIA's TESLA and/or QUADRO GPUs) mayinclude any number of GPUs that may be used for executing processingtasks of compute services 3616, AI services 3618, visualization services3620, other services, and/or any of features or functionality ofsoftware 3518. For example, with respect to AI services 3618, GPUs 3622may be used to perform pre-processing on imaging data (or other datatypes used by machine learning models), post-processing on outputs ofmachine learning models, and/or to perform inferencing (e.g., to executemachine learning models). In at least one embodiment, cloud 3626, AIsystem 3624, and/or other components of system 3600 may use GPUs 3622.In at least one embodiment, cloud 3626 may include a GPU-optimizedplatform for deep learning tasks. In at least one embodiment, AI system3624 may use GPUs, and cloud 3626—or at least a portion tasked with deeplearning or inferencing—may be executed using one or more AI systems3624. As such, although hardware 3522 is illustrated as discretecomponents, this is not intended to be limiting, and any components ofhardware 3522 may be combined with, or leveraged by, any othercomponents of hardware 3522.

In at least one embodiment, AI system 3624 may include a purpose-builtcomputing system (e.g., a super-computer or an HPC) configured forinferencing, deep learning, machine learning, and/or other artificialintelligence tasks. In at least one embodiment, AI system 3624 (e.g.,NVIDIA's DGX) may include GPU-optimized software (e.g., a softwarestack) that may be executed using a plurality of GPUs 3622, in additionto CPUs, RAM, storage, and/or other components, features, orfunctionality. In at least one embodiment, one or more AI systems 3624may be implemented in cloud 3626 (e.g., in a data center) for performingsome or all of AI-based processing tasks of system 3600.

In at least one embodiment, cloud 3626 may include a GPU-acceleratedinfrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimizedplatform for executing processing tasks of system 3600. In at least oneembodiment, cloud 3626 may include an AI system(s) 3624 for performingone or more of AI-based tasks of system 3600 (e.g., as a hardwareabstraction and scaling platform). In at least one embodiment, cloud3626 may integrate with application orchestration system 3628 leveragingmultiple GPUs to enable seamless scaling and load balancing between andamong applications and services 3520. In at least one embodiment, cloud3626 may tasked with executing at least some of services 3520 of system3600, including compute services 3616, AI services 3618, and/orvisualization services 3620, as described herein. In at least oneembodiment, cloud 3626 may perform small and large batch inference(e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallelcomputing API and platform 3630 (e.g., NVIDIA's CUDA), executeapplication orchestration system 3628 (e.g., KUBERNETES), provide agraphics rendering API and platform (e.g., for ray-tracing, 2D graphics,3D graphics, and/or other rendering techniques to produce higher qualitycinematics), and/or may provide other functionality for system 3600.

In at least one embodiment, in an effort to preserve patientconfidentiality (e.g., where patient data or records are to be usedoff-premises), cloud 3626 may include a registry—such as a deep learningcontainer registry. In at least one embodiment, a registry may storecontainers for instantiations of applications that may performpre-processing, post-processing, or other processing tasks on patientdata. In at least one embodiment, cloud 3626 may receive data thatincludes patient data as well as sensor data in containers, performrequested processing for just sensor data in those containers, and thenforward a resultant output and/or visualizations to appropriate partiesand/or devices (e.g., on-premises medical devices used for visualizationor diagnoses), all without having to extract, store, or otherwise accesspatient data. In at least one embodiment, confidentiality of patientdata is preserved in compliance with HIPAA and/or other dataregulations.

In at least one embodiment, one or more systems depicted in FIG. 36 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 36 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 36 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 37 includes an example illustration of a deployment pipeline 3610Afor processing imaging data, in accordance with at least one embodiment.In at least one embodiment, system 3600—and specifically deploymentsystem 3506—may be used to customize, update, and/or integratedeployment pipeline(s) 3610A into one or more production environments.In at least one embodiment, deployment pipeline 3610A of FIG. 37includes a non-limiting example of a deployment pipeline 3610A that maybe custom defined by a particular user (or team of users) at a facility(e.g., at a hospital, clinic, lab, research environment, etc.). In atleast one embodiment, to define deployment pipelines 3610A for a CTscanner 3702, a user may select—from a container registry, forexample—one or more applications that perform specific functions ortasks with respect to imaging data generated by CT scanner 3702. In atleast one embodiment, applications may be applied to deployment pipeline3610A as containers that may leverage services 3520 and/or hardware 3522of system 3600. In addition, deployment pipeline 3610A may includeadditional processing tasks or applications that may be implemented toprepare data for use by applications (e.g., DICOM adapter 3602B andDICOM reader 3706 may be used in deployment pipeline 3610A to preparedata for use by CT reconstruction 3708, organ segmentation 3710, etc.).In at least one embodiment, deployment pipeline 3610A may be customizedor selected for consistent deployment, one time use, or for anotherfrequency or interval. In at least one embodiment, a user may desire tohave CT reconstruction 3708 and organ segmentation 3710 for severalsubjects over a specific interval, and thus may deploy pipeline 3610Afor that period of time. In at least one embodiment, a user may select,for each request from system 3600, applications that a user wants toperform processing on that data for that request. In at least oneembodiment, deployment pipeline 3610A may be adjusted at any intervaland, because of adaptability and scalability of a container structurewithin system 3600, this may be a seamless process.

In at least one embodiment, deployment pipeline 3610A of FIG. 37 mayinclude CT scanner 3702 generating imaging data of a patient or subject.In at least one embodiment, imaging data from CT scanner 3702 may bestored on a PACS server(s) 3704 associated with a facility housing CTscanner 3702. In at least one embodiment, PACS server(s) 3704 mayinclude software and/or hardware components that may directly interfacewith imaging modalities (e.g., CT scanner 3702) at a facility. In atleast one embodiment, DICOM adapter 3602B may enable sending and receiptof DICOM objects using DICOM protocols. In at least one embodiment,DICOM adapter 3602B may aid in preparation or configuration of DICOMdata from PACS server(s) 3704 for use by deployment pipeline 3610A. Inat least one embodiment, once DICOM data is processed through DICOMadapter 3602B, pipeline manager 3612 may route data through todeployment pipeline 3610A. In at least one embodiment, DICOM reader 3706may extract image files and any associated metadata from DICOM data(e.g., raw sinogram data, as illustrated in visualization 3716A). In atleast one embodiment, working files that are extracted may be stored ina cache for faster processing by other applications in deploymentpipeline 3610A. In at least one embodiment, once DICOM reader 3706 hasfinished extracting and/or storing data, a signal of completion may becommunicated to pipeline manager 3612. In at least one embodiment,pipeline manager 3612 may then initiate or call upon one or more otherapplications or containers in deployment pipeline 3610A.

In at least one embodiment, CT reconstruction 3708 application and/orcontainer may be executed once data (e.g., raw sinogram data) isavailable for processing by CT reconstruction 3708 application. In atleast one embodiment, CT reconstruction 3708 may read raw sinogram datafrom a cache, reconstruct an image file out of raw sinogram data (e.g.,as illustrated in visualization 3716B), and store resulting image filein a cache. In at least one embodiment, at completion of reconstruction,pipeline manager 3612 may be signaled that reconstruction task iscomplete. In at least one embodiment, once reconstruction is complete,and a reconstructed image file may be stored in a cache (or otherstorage device), organ segmentation 3710 application and/or containermay be triggered by pipeline manager 3612. In at least one embodiment,organ segmentation 3710 application and/or container may read an imagefile from a cache, normalize or convert an image file to format suitablefor inference (e.g., convert an image file to an input resolution of amachine learning model), and run inference against a normalized image.In at least one embodiment, to run inference on a normalized image,organ segmentation 3710 application and/or container may rely onservices 3520, and pipeline manager 3612 and/or applicationorchestration system 3628 may facilitate use of services 3520 by organsegmentation 3710 application and/or container. In at least oneembodiment, for example, organ segmentation 3710 application and/orcontainer may leverage AI services 3618 to perform inference on anormalized image, and AI services 3618 may leverage hardware 3522 (e.g.,AI system 3624) to execute AI services 3618. In at least one embodiment,a result of an inference may be a mask file (e.g., as illustrated invisualization 3716C) that may be stored in a cache (or other storagedevice).

In at least one embodiment, once applications that process DICOM dataand/or data extracted from DICOM data have completed processing, asignal may be generated for pipeline manager 3612. In at least oneembodiment, pipeline manager 3612 may then execute DICOM writer 3712 toread results from a cache (or other storage device), package resultsinto a DICOM format (e.g., as DICOM output 3714) for use by users at afacility who generated a request. In at least one embodiment, DICOMoutput 3714 may then be transmitted to DICOM adapter 3602B to prepareDICOM output 3714 for storage on PACS server(s) 3704 (e.g., for viewingby a DICOM viewer at a facility). In at least one embodiment, inresponse to a request for reconstruction and segmentation,visualizations 3716B and 3716C may be generated and available to a userfor diagnoses, research, and/or for other purposes.

Although illustrated as consecutive application in deployment pipeline3610A, CT reconstruction 3708 and organ segmentation 3710 applicationsmay be processed in parallel in at least one embodiment. In at least oneembodiment, where applications do not have dependencies on one another,and data is available for each application (e.g., after DICOM reader3706 extracts data), applications may be executed at a same time,substantially at a same time, or with some overlap. In at least oneembodiment, where two or more applications require similar services3520, a scheduler of system 3600 may be used to load balance anddistribute compute or processing resources between and among variousapplications. In at least one embodiment, in some embodiments, parallelcomputing platform 3630 may be used to perform parallel processing forapplications to decrease run-time of deployment pipeline 3610A toprovide real-time results.

In at least one embodiment, and with reference to FIGS. 38A-38B,deployment system 3506 may be implemented as one or more virtualinstruments to perform different functionalities—such as imageprocessing, segmentation, enhancement, AI, visualization, andinferencing—with imaging devices (e.g., CT scanners, X-ray machines, MRImachines, etc.), sequencing devices, genomics devices, and/or otherdevice types. In at least one embodiment, system 3600 may allow forcreation and provision of virtual instruments that may include asoftware-defined deployment pipeline 3610 that may receiveraw/unprocessed input data generated by a device(s) and outputprocessed/reconstructed data. In at least one embodiment, deploymentpipelines 3610 (e.g., 3610A and 3610B) that represent virtualinstruments may implement intelligence into a pipeline, such as byleveraging machine learning models, to provide containerized inferencesupport to a system. In at least one embodiment, virtual instruments mayexecute any number of containers each including instantiations ofapplications. In at least one embodiment, such as where real-timeprocessing is desired, deployment pipelines 3610 representing virtualinstruments may be static (e.g., containers and/or applications may beset), while in other examples, container and/or applications for virtualinstruments may be selected (e.g., on a per-request basis) from a poolof applications or resources (e.g., within a container registry).

In at least one embodiment, system 3600 may be instantiated or executedas one or more virtual instruments on-premise at a facility in, forexample, a computing system deployed next to or otherwise incommunication with a radiology machine, an imaging device, and/oranother device type at a facility. In at least one embodiment, however,an on-premise installation may be instantiated or executed within acomputing system of a device itself (e.g., a computing system integralto an imaging device), in a local datacenter (e.g., a datacenteron-premise), and/or in a cloud-environment (e.g., in cloud 3626). In atleast one embodiment, deployment system 3506, operating as a virtualinstrument, may be instantiated by a supercomputer or other HPC systemin some examples. In at least one embodiment, on-premise installationmay allow for high-bandwidth uses (via, for example, higher throughputlocal communication interfaces, such as RF over Ethernet) for real-timeprocessing. In at least one embodiment, real-time or near real-timeprocessing may be particularly useful where a virtual instrumentsupports an ultrasound device or other imaging modality where immediatevisualizations are expected or required for accurate diagnoses andanalyses. In at least one embodiment, a cloud-computing architecture maybe capable of dynamic bursting to a cloud computing service provider, orother compute cluster, when local demand exceeds on-premise capacity orcapability. In at least one embodiment, a cloud architecture, whenimplemented, may be tuned for training neural networks or other machinelearning models, as described herein with respect to training system3504. In at least one embodiment, with training pipelines in place,machine learning models may be continuously learn and improve as theyprocess additional data from devices they support. In at least oneembodiment, virtual instruments may be continually improved usingadditional data, new data, existing machine learning models, and/or newor updated machine learning models.

In at least one embodiment, a computing system may include some or allof hardware 3522 described herein, and hardware 3522 may be distributedin any of a number of ways including within a device, as part of acomputing device coupled to and located proximate a device, in a localdatacenter at a facility, and/or in cloud 3626. In at least oneembodiment, because deployment system 3506 and associated applicationsor containers are created in software (e.g., as discrete containerizedinstantiations of applications), behavior, operation, and configurationof virtual instruments, as well as outputs generated by virtualinstruments, may be modified or customized as desired, without having tochange or alter raw output of a device that a virtual instrumentsupports.

In at least one embodiment, one or more systems depicted in FIG. 37 areutilized to implement an attention-on-label training process. In atleast one embodiment, one or more systems depicted in FIG. 37 areutilized to implement one or more networks and training schemes such asthose described in connection with FIG. 1 and FIG. 2. In at least oneembodiment, one or more systems depicted in FIG. 37 are utilized toimplement a learning method that utilizes meta-training with gradientsfrom various label sets of training data to select a label for finalgradient back-propagation.

FIG. 38A includes an example data flow diagram of a virtual instrumentsupporting an ultrasound device, in accordance with at least oneembodiment. In at least one embodiment, deployment pipeline 3610B mayleverage one or more of services 3520 of system 3600. In at least oneembodiment, deployment pipeline 3610B and services 3520 may leveragehardware 3522 of a system either locally or in cloud 3626. In at leastone embodiment, although not illustrated, process 3800 may befacilitated by pipeline manager 3612, application orchestration system3628, and/or parallel computing platform 3630.

In at least one embodiment, process 3800 may include receipt of imagingdata from an ultrasound device 3802. In at least one embodiment, imagingdata may be stored on PACS server(s) in a DICOM format (or other format,such as RIS, CIS, REST compliant, RPC, raw, etc.), and may be receivedby system 3600 for processing through deployment pipeline 3610 selectedor customized as a virtual instrument (e.g., a virtual ultrasound) forultrasound device 3802. In at least one embodiment, imaging data may bereceived directly from an imaging device (e.g., ultrasound device 3802)and processed by a virtual instrument. In at least one embodiment, atransducer or other signal converter communicatively coupled between animaging device and a virtual instrument may convert signal datagenerated by an imaging device to image data that may be processed by avirtual instrument. In at least one embodiment, raw data and/or imagedata may be applied to DICOM reader 3706 to extract data for use byapplications or containers of deployment pipeline 3610B. In at least oneembodiment, DICOM reader 3706 may leverage data augmentation library3814 (e.g., NVIDIA's DALI) as a service 3520 (e.g., as one of computeservice(s) 3616) for extracting, resizing, rescaling, and/or otherwisepreparing data for use by applications or containers.

In at least one embodiment, once data is prepared, a reconstruction 3806application and/or container may be executed to reconstruct data fromultrasound device 3802 into an image file. In at least one embodiment,after reconstruction 3806, or at a same time as reconstruction 3806, adetection 3808 application and/or container may be executed for anomalydetection, object detection, feature detection, and/or other detectiontasks related to data. In at least one embodiment, an image filegenerated during reconstruction 3806 may be used during detection 3808to identify anomalies, objects, features, etc. In at least oneembodiment, detection 3808 application may leverage an inference engine3816 (e.g., as one of AI service(s) 3618) to perform inference on datato generate detections. In at least one embodiment, one or more machinelearning models (e.g., from training system 3504) may be executed orcalled by detection 3808 application.

In at least one embodiment, once reconstruction 3806 and/or detection3808 is/are complete, data output from these application and/orcontainers may be used to generate visualizations 3810, such asvisualization 3812 (e.g., a grayscale output) displayed on a workstationor display terminal. In at least one embodiment, visualization may allowa technician or other user to visualize results of deployment pipeline3610B with respect to ultrasound device 3802. In at least oneembodiment, visualization 3810 may be executed by leveraging a rendercomponent 3818 of system 3600 (e.g., one of visualization service(s)3620). In at least one embodiment, render component 3818 may execute a2D, OpenGL, or ray-tracing service to generate visualization 3812.

FIG. 38B includes an example data flow diagram of a virtual instrumentsupporting a CT scanner, in accordance with at least one embodiment. Inat least one embodiment, deployment pipeline 3610C may leverage one ormore of services 3520 of system 3600. In at least one embodiment,deployment pipeline 3610C and services 3520 may leverage hardware 3522of a system either locally or in cloud 3626. In at least one embodiment,although not illustrated, process 3820 may be facilitated by pipelinemanager 3612, application orchestration system 3628, and/or parallelcomputing platform 3630.

In at least one embodiment, process 3820 may include CT scanner 3822generating raw data that may be received by DICOM reader 3706 (e.g.,directly, via a PACS server 3704, after processing, etc.). In at leastone embodiment, a Virtual CT (instantiated by deployment pipeline 3610C)may include a first, real-time pipeline for monitoring a patient (e.g.,patient movement detection AI 3826) and/or for adjusting or optimizingexposure of CT scanner 3822 (e.g., using exposure control AI 3824). Inat least one embodiment, one or more of applications (e.g., 3824 and3826) may leverage a service 3520, such as AI service(s) 3618. In atleast one embodiment, outputs of exposure control AI 3824 application(or container) and/or patient movement detection AI 3826 application (orcontainer) may be used as feedback to CT scanner 3822 and/or atechnician for adjusting exposure (or other settings of CT scanner 3822)and/or informing a patient to move less.

In at least one embodiment, deployment pipeline 3610C may include anon-real-time pipeline for analyzing data generated by CT scanner 3822.In at least one embodiment, a second pipeline may include CTreconstruction 3708 application and/or container, a coarse detection AI3828 application and/or container, a fine detection AI 3832 applicationand/or container (e.g., where certain results are detected by coarsedetection AI 3828), a visualization 3830 application and/or container,and a DICOM writer 3712 (and/or other data type writer, such as RIS,CIS, REST compliant, RPC, raw, etc.) application and/or container. In atleast one embodiment, raw data generated by CT scanner 3822 may bepassed through pipelines of deployment pipeline 3610C (instantiated as avirtual CT instrument) to generate results. In at least one embodiment,results from DICOM writer 3712 may be transmitted for display and/or maybe stored on PACS server(s) 3704 for later retrieval, analysis, ordisplay by a technician, practitioner, or other user.

In at least one embodiment, one or more systems depicted in FIG.38A-FIG. 38B are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 38A-FIG. 38B are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.38A-FIG. 38B are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

FIG. 39A illustrates a data flow diagram for a process 3900 to train,retrain, or update a machine learning model, in accordance with at leastone embodiment. In at least one embodiment, process 3900 may be executedusing, as a non-limiting example, system 3600 of FIG. 36. In at leastone embodiment, process 3900 may leverage services 3520 and/or hardware3522 of system 3600, as described herein. In at least one embodiment,refined models 3912 generated by process 3900 may be executed bydeployment system 3506 for one or more containerized applications indeployment pipelines 3610.

In at least one embodiment, model training 3514 may include retrainingor updating an initial model 3904 (e.g., a pre-trained model) using newtraining data (e.g., new input data, such as customer dataset 3906,and/or new ground truth data associated with input data). In at leastone embodiment, to retrain, or update, initial model 3904, output orloss layer(s) of initial model 3904 may be reset, or deleted, and/orreplaced with an updated or new output or loss layer(s). In at least oneembodiment, initial model 3904 may have previously fine-tuned parameters(e.g., weights and/or biases) that remain from prior training, sotraining or retraining 3514 may not take as long or require as muchprocessing as training a model from scratch. In at least one embodiment,during model training 3514, by having reset or replaced output or losslayer(s) of initial model 3904, parameters may be updated and re-tunedfor a new data set based on loss calculations associated with accuracyof output or loss layer(s) at generating predictions on new, customerdataset 3906 (e.g., image data 3508 of FIG. 35).

In at least one embodiment, pre-trained models 3606 may be stored in adata store, or registry (e.g., model registry 3524 of FIG. 35). In atleast one embodiment, pre-trained models 3606 may have been trained, atleast in part, at one or more facilities other than a facility executingprocess 3900. In at least one embodiment, to protect privacy and rightsof patients, subjects, or clients of different facilities, pre-trainedmodels 3606 may have been trained, on-premise, using customer or patientdata generated on-premise. In at least one embodiment, pre-trainedmodels 3606 may be trained using cloud 3626 and/or other hardware 3522,but confidential, privacy protected patient data may not be transferredto, used by, or accessible to any components of cloud 3626 (or other offpremise hardware). In at least one embodiment, where a pre-trained model3606 is trained at using patient data from more than one facility,pre-trained model 3606 may have been individually trained for eachfacility prior to being trained on patient or customer data from anotherfacility. In at least one embodiment, such as where a customer orpatient data has been released of privacy concerns (e.g., by waiver, forexperimental use, etc.), or where a customer or patient data is includedin a public data set, a customer or patient data from any number offacilities may be used to train pre-trained model 3606 on-premise and/oroff premise, such as in a datacenter or other cloud computinginfrastructure.

In at least one embodiment, when selecting applications for use indeployment pipelines 3610, a user may also select machine learningmodels to be used for specific applications. In at least one embodiment,a user may not have a model for use, so a user may select a pre-trainedmodel 3606 to use with an application. In at least one embodiment,pre-trained model 3606 may not be optimized for generating accurateresults on customer dataset 3906 of a facility of a user (e.g., based onpatient diversity, demographics, types of medical imaging devices used,etc.). In at least one embodiment, prior to deploying pre-trained model3606 into deployment pipeline 3610 for use with an application(s),pre-trained model 3606 may be updated, retrained, and/or fine-tuned foruse at a respective facility.

In at least one embodiment, a user may select pre-trained model 3606that is to be updated, retrained, and/or fine-tuned, and pre-trainedmodel 3606 may be referred to as initial model 3904 for training system3504 within process 3900. In at least one embodiment, customer dataset3906 (e.g., imaging data, genomics data, sequencing data, or other datatypes generated by devices at a facility) may be used to perform modeltraining 3514 (which may include, without limitation, transfer learning)on initial model 3904 to generate refined model 3912. In at least oneembodiment, ground truth data corresponding to customer dataset 3906 maybe generated by training system 3504. In at least one embodiment, groundtruth data may be generated, at least in part, by clinicians,scientists, doctors, practitioners, at a facility (e.g., as labeledclinic data 3512 of FIG. 35).

In at least one embodiment, AI-assisted annotation 3510 may be used insome examples to generate ground truth data. In at least one embodiment,AI-assisted annotation 3510 (e.g., implemented using an AI-assistedannotation SDK) may leverage machine learning models (e.g., neuralnetworks) to generate suggested or predicted ground truth data for acustomer dataset. In at least one embodiment, user 3910 may useannotation tools within a user interface (a graphical user interface(GUI)) on computing device 3908.

In at least one embodiment, user 3910 may interact with a GUI viacomputing device 3908 to edit or fine-tune annotations orauto-annotations. In at least one embodiment, a polygon editing featuremay be used to move vertices of a polygon to more accurate or fine-tunedlocations.

In at least one embodiment, once customer dataset 3906 has associatedground truth data, ground truth data (e.g., from AI-assisted annotation,manual labeling, etc.) may be used by during model training 3514 togenerate refined model 3912. In at least one embodiment, customerdataset 3906 may be applied to initial model 3904 any number of times,and ground truth data may be used to update parameters of initial model3904 until an acceptable level of accuracy is attained for refined model3912. In at least one embodiment, once refined model 3912 is generated,refined model 3912 may be deployed within one or more deploymentpipelines 3610 at a facility for performing one or more processing taskswith respect to medical imaging data.

In at least one embodiment, refined model 3912 may be uploaded topre-trained models 3606 in model registry 3524 to be selected by anotherfacility. In at least one embodiment, his process may be completed atany number of facilities such that refined model 3912 may be furtherrefined on new datasets any number of times to generate a more universalmodel.

FIG. 39B is an example illustration of a client-server architecture 3932to enhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment. In at least one embodiment,AI-assisted annotation tools 3936 may be instantiated based on aclient-server architecture 3932. In at least one embodiment, annotationtools 3936 in imaging applications may aid radiologists, for example,identify organs and abnormalities. In at least one embodiment, imagingapplications may include software tools that help user 3910 to identify,as a non-limiting example, a few extreme points on a particular organ ofinterest in raw images 3934 (e.g., in a 3D MRI or CT scan) and receiveauto-annotated results for all 2D slices of a particular organ. In atleast one embodiment, results may be stored in a data store as trainingdata 3938 and used as (for example and without limitation) ground truthdata for training. In at least one embodiment, when computing device3908 sends extreme points for AI-assisted annotation 3510, a deeplearning model, for example, may receive this data as input and returninference results of a segmented organ or abnormality. In at least oneembodiment, pre-instantiated annotation tools, such as AI-AssistedAnnotation Tool 3936B in FIG. 39B, may be enhanced by making API calls(e.g., API Call 3944) to a server, such as an Annotation AssistantServer 3940 that may include a set of pre-trained models 3942 stored inan annotation model registry, for example. In at least one embodiment,an annotation model registry may store pre-trained models 3942 (e.g.,machine learning models, such as deep learning models) that arepre-trained to perform AI-assisted annotation on a particular organ orabnormality. In at least one embodiment, these models may be furtherupdated by using training pipelines 3604. In at least one embodiment,pre-installed annotation tools may be improved over time as new labeledclinic data 3512 is added.

Inference and/or training logic 615 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 615 are providedherein in conjunction with FIGS. 6A and/or 6B.

In at least one embodiment, one or more systems depicted in FIG.39A-FIG. 39B are utilized to implement an attention-on-label trainingprocess. In at least one embodiment, one or more systems depicted inFIG. 39A-FIG. 39B are utilized to implement one or more networks andtraining schemes such as those described in connection with FIG. 1 andFIG. 2. In at least one embodiment, one or more systems depicted in FIG.39A-FIG. 39B are utilized to implement a learning method that utilizesmeta-training with gradients from various label sets of training data toselect a label for final gradient back-propagation.

At least one embodiment of the disclosure can be described in view ofthe following clauses:

-   -   1. A processor, comprising:    -   one or more circuits to train one or more neural networks to        select a label from one or more labels available for a training        image, wherein the selected label and the training image is to        train the one or more neural networks.    -   2. The processor of clause 1, wherein training the one or more        neural networks further comprises the one or more circuits to:    -   obtain a batch of data comprising a portion of the one or more        labels and a plurality of training images;    -   generate a set of weights for the one or more neural networks by        processing the batch;    -   compute, using the set of weights for the one or more neural        networks, a set of features representing characteristics        corresponding to a label set of the batch of data;    -   concatenate the features to generate a feature set;    -   compute a weight for each feature set;    -   compute a weighted average, using the weight for each feature        set, for the portion of the one or more labels; and    -   select the label, based on the weighted average for the portion        of the one or more labels, to update the one or more neural        networks.    -   3. The processor of any of clauses 1 or 2, wherein the weights        for each feature set is computed through one or more        fully-connected layers and one or more activation functions.    -   4. The processor of clause 1, wherein the one or more labels are        generated by different algorithm-based labelers.    -   5. The processor of clause 1, where the one or more circuits are        further to:    -   perform a comparison of an output of the one or more neural        networks being trained with labels with the one or more labels;        and    -   select the label based at least in part on the comparison.    -   6. The processor of any of clauses 1 or 5, wherein the output is        compared through one or more binary cross entropy (BCE) loss        functions.    -   7. The processor of clause 1, wherein the one or more neural        networks uses the selected label and the training image to        perform one or more image classification tasks.    -   8. A system, comprising:    -   one or more computers having one or more processors to train one        or more neural networks to select labels to be used while        training the one or more neural networks.    -   9. The system of clause 8, wherein training the one or more        neural networks further comprises the one or more processors to:    -   provide a plurality of images associated with the labels to the        one or more neural networks; and    -   select the labels for an image of the plurality of images to        train the one or more neural networks.    -   10. The system of any of clauses 8 or 9, wherein selecting the        labels for the image of the plurality of images to train the one        or more neural networks further comprises the one or more        processors to:    -   receive a portion of training data comprising one or more labels        available for the image;    -   generate a set of weights for the one or more neural networks by        processing the portion;    -   compute, using the set of weights, features representing        characteristics corresponding to the labels;    -   use the features to compute weighted averages for the labels;        and    -   select the labels from the labels based on the weighted        averages.    -   11. The system of any of clauses 8-10, wherein the set of        weights for the one or more neural networks are generated        through one or more back-propagation processes.    -   12. The system of any of clauses 8-10, wherein one or more        differentiable binarization processes are performed on the        weighted averages.    -   13. The system of clause 8, wherein the labels are generated by        different natural language processing (NLP) algorithms executed        by different computing resources.    -   14. A machine-readable medium having stored thereon a set of        instructions, which if performed by one or more processors,        cause the one or more processors to train one or more neural        networks to select labels to be used while training the one or        more neural networks.    -   15. The machine-readable medium of clause 14, wherein training        the one or more neural networks further causes the one or more        processors to:    -   receive data as input to train the one or more neural networks;    -   generate a set of model weights for the one or more neural        networks by processing a portion of the data, wherein the        portion of the data comprises one or more labels available for a        training image;    -   compute, based on the set of model weights, a set of features        representing characteristics corresponding to a label set of the        portion;    -   use the set of features to compute weights for each label from        the label set; and    -   select the labels from the label set based on the weights to        update parameters of the one or more neural networks.    -   16. The machine-readable medium of any of clauses 14 or 15,        wherein training the one or more neural networks further causes        the one or more processors to:    -   perform a comparison of with an output of the one or more neural        networks with the label set; and    -   select the labels based in part on the comparison.    -   17. The machine-readable medium of any of clauses 14-16, wherein        the comparison is performed using one or more loss functions.    -   18. The machine-readable medium of any of clauses 14 or 15,        wherein the one or more labels are generated by one or more        algorithm-based labelers.    -   19. The machine-readable medium of any of clauses 14 or 15,        wherein the selected labels and the training image train the one        or more neural networks to perform one or more multi-label        classification tasks.    -   20. The machine-readable medium of any of clauses 14 or 15,        wherein the set of model weights are generated through one or        more back-propagation processes.    -   21. A processor, comprising:    -   one or more circuits to use a neural network to infer        information, wherein the neural network is trained by selecting        labels to be used while training the neural network.    -   22. The processor of clause 21, wherein the one or more circuits        are further to:    -   determine a batch from training data, received as input to the        neural network, comprising one or more labels available for a        training image;    -   generate model weights for the neural network by processing the        batch;    -   compute, using the model weights, features representing        characteristics of the one or more labels;    -   use the features to compute weighted averages for the one or        more labels; and    -   select the labels from the one or more labels based on the        weighted averages.    -   23. The processor of any of clauses 21 or 22, wherein the one or        more circuits are further to update parameters of the neural        network using the selected labels to perform image segmentation        tasks.    -   24. The processor of any of clauses 21 or 22, wherein training        data comprises a plurality of training images, wherein each of        the plurality of training images is accompanied with a text        report.    -   25. The processor of any of clauses 21, 22 or 24, wherein the        one or more circuits are further to:    -   generate text embedding based on the text report; and    -   use the text embedding to identify the one or more labels.    -   26. The processor of clause 21, wherein the neural network is        trained by selecting labels to be used while simultaneously        training the neural network to classify training images and        associated labels.    -   27. A system, comprising:    -   one or more computers having one or more processors to use a        neural network to infer information, wherein the neural network        is trained by selecting labels to be used while training the        neural network.    -   28. The system of clause 27, further comprising one or more        computers having one or more processors to train the neural        network to select labels while simultaneously training the        neural network to classify training images.    -   29. The system of any of clauses 27 or 28, wherein training the        neural network to select labels further comprises the one or        more processors to:    -   receive data as input to the neural network;    -   generate a set of weights for the neural network by processing a        subset of the data, wherein the subset of the data comprises one        or more labels available for a training image;    -   compute, based on the set of weights, a set of features        representing characteristics corresponding to a label set of the        subset;    -   use the set of features to compute weights for each label from        the subset; and    -   update a component of the neural network using a portion of the        labels from the label set based on the computed weights for each        label.    -   30. The system of any of clauses 27-29, wherein the one or more        labels are generated by one or more natural-language processing        (NLP) algorithms performed by different processors.    -   31. The system of any of clauses 27-30, wherein the portion of        the labels and the training image are used by the neural network        to perform one or more image classification tasks.    -   32. A machine-readable medium having stored thereon a set of        instructions, which if performed by one or more processors,        cause the one or more processors to use a neural network to        infer information, wherein the neural network is trained by        selecting labels to be used while training the neural network.    -   33. The machine-readable medium of clause 32, wherein training        the neural network further causes the one or more processors to:    -   use training data, received as input to the neural network,        comprising a plurality of labels available for a plurality of        training images to determine labels for a training image; and    -   use the labels for the training image to update parameters of        the neural network.    -   34. The machine-readable medium of any of clauses 32 or 33,        wherein parameters of the neural network is updated based on        results from performing a comparison of the labels with an        output of the neural network.    -   35. The machine-readable medium of any of clauses 32 or 33,        wherein the training data comprising a plurality of labels for a        plurality of images is extracted from text reports accompanying        each training image of the plurality of images.    -   36. The machine-readable medium of any of clauses 32, 33, or 35,        wherein text reports accompanying each training image of the        plurality of images are generated by one or more        natural-language processing (NLP) algorithms performed by        different computing resources.

In at least one embodiment, a single semiconductor platform may refer toa sole unitary semiconductor-based integrated circuit or chip. In atleast one embodiment, multi-chip modules may be used with increasedconnectivity which simulate on-chip operation, and make substantialimprovements over utilizing a conventional central processing unit(“CPU”) and bus implementation. In at least one embodiment, variousmodules may also be situated separately or in various combinations ofsemiconductor platforms per desires of user.

In at least one embodiment, referring back to FIG. 12, computer programsin form of machine-readable executable code or computer control logicalgorithms are stored in main memory 1204 and/or secondary storage.Computer programs, if executed by one or more processors, enable system1200 to perform various functions in accordance with at least oneembodiment. In at least one embodiment, memory 1204, storage, and/or anyother storage are possible examples of computer-readable media. In atleast one embodiment, secondary storage may refer to any suitablestorage device or system such as a hard disk drive and/or a removablestorage drive, representing a floppy disk drive, a magnetic tape drive,a compact disk drive, digital versatile disk (“DVD”) drive, recordingdevice, universal serial bus (“USB”) flash memory, etc. In at least oneembodiment, architecture and/or functionality of various previousfigures are implemented in context of CPU 1202, parallel processingsystem 1212, an integrated circuit capable of at least a portion ofcapabilities of both CPU 1202, parallel processing system 1212, achipset (e.g., a group of integrated circuits designed to work and soldas a unit for performing related functions, etc.), and/or any suitablecombination of integrated circuit(s).

In at least one embodiment, architecture and/or functionality of variousprevious figures are implemented in context of a general computersystem, a circuit board system, a game console system dedicated forentertainment purposes, an application-specific system, and more. In atleast one embodiment, computer system 1200 may take form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (“PDA”), a digital camera, a vehicle, a head mounted display,a hand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

In at least one embodiment, parallel processing system 1212 includes,without limitation, a plurality of parallel processing units (“PPUs”)1214 and associated memories 1216. In at least one embodiment, PPUs 1214are connected to a host processor or other peripheral devices via aninterconnect 1218 and a switch 1220 or multiplexer. In at least oneembodiment, parallel processing system 1212 distributes computationaltasks across PPUs 1214 which can be parallelizable—for example, as partof distribution of computational tasks across multiple graphicsprocessing unit (“GPU”) thread blocks. In at least one embodiment,memory is shared and accessible (e.g., for read and/or write access)across some or all of PPUs 1214, although such shared memory may incurperformance penalties relative to use of local memory and registersresident to a PPU 1214. In at least one embodiment, operation of PPUs1214 is synchronized through use of a command such as _syncthreads( ),wherein all threads in a block (e.g., executed across multiple PPUs1214) to reach a certain point of execution of code before proceeding.

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. “Connected,”when unmodified and referring to physical connections, is to beconstrued as partly or wholly contained within, attached to, or joinedtogether, even if there is something intervening. Recitation of rangesof values herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within range,unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. In at least one embodiment, use of term “set” (e.g., “a set ofitems”) or “subset” unless otherwise noted or contradicted by context,is to be construed as a nonempty collection comprising one or moremembers. Further, unless otherwise noted or contradicted by context,term “subset” of a corresponding set does not necessarily denote aproper subset of corresponding set, but subset and corresponding set maybe equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. For instance, in illustrative example of a set havingthree members, conjunctive phrases “at least one of A, B, and C” and “atleast one of A, B and C” refer to any of following sets: {A}, {B}, {C},{A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language isnot generally intended to imply that certain embodiments require atleast one of A, at least one of B and at least one of C each to bepresent. In addition, unless otherwise noted or contradicted by context,term “plurality” indicates a state of being plural (e.g., “a pluralityof items” indicates multiple items). In at least one embodiment, numberof items in a plurality is at least two, but can be more when soindicated either explicitly or by context. Further, unless statedotherwise or otherwise clear from context, phrase “based on” means“based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium, for example, in form of acomputer program comprising a plurality of instructions executable byone or more processors. In at least one embodiment, a computer-readablestorage medium is a non-transitory computer-readable storage medium thatexcludes transitory signals (e.g., a propagating transient electric orelectromagnetic transmission) but includes non-transitory data storagecircuitry (e.g., buffers, cache, and queues) within transceivers oftransitory signals. In at least one embodiment, code (e.g., executablecode or source code) is stored on a set of one or more non-transitorycomputer-readable storage media having stored thereon executableinstructions (or other memory to store executable instructions) that,when executed (i.e., as a result of being executed) by one or moreprocessors of a computer system, cause computer system to performoperations described herein. In at least one embodiment, set ofnon-transitory computer-readable storage media comprises multiplenon-transitory computer-readable storage media and one or more ofindividual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by differentprocessors—for example, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofdisclosure and does not pose a limitation on scope of disclosure unlessotherwise claimed. No language in specification should be construed asindicating any non-claimed element as essential to practice ofdisclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in particularexamples, “connected” or “coupled” may be used to indicate that two ormore elements are in direct or indirect physical or electrical contactwith each other. “Coupled” may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting examples,“processor” may be a CPU or a GPU. A “computing platform” may compriseone or more processors. As used herein, “software” processes mayinclude, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.In at least one embodiment, terms “system” and “method” are used hereininterchangeably insofar as system may embody one or more methods andmethods may be considered a system.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. In at least oneembodiment, process of obtaining, acquiring, receiving, or inputtinganalog and digital data can be accomplished in a variety of ways such asby receiving data as a parameter of a function call or a call to anapplication programming interface. In at least one embodiment, processesof obtaining, acquiring, receiving, or inputting analog or digital datacan be accomplished by transferring data via a serial or parallelinterface. In at least one embodiment, processes of obtaining,acquiring, receiving, or inputting analog or digital data can beaccomplished by transferring data via a computer network from providingentity to acquiring entity. In at least one embodiment, references mayalso be made to providing, outputting, transmitting, sending, orpresenting analog or digital data. In various examples, processes ofproviding, outputting, transmitting, sending, or presenting analog ordigital data can be accomplished by transferring data as an input oroutput parameter of a function call, a parameter of an applicationprogramming interface or interprocess communication mechanism.

Although descriptions herein set forth example implementations ofdescribed techniques, other architectures may be used to implementdescribed functionality, and are intended to be within scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities may be defined above for purposes of description,various functions and responsibilities might be distributed and dividedin different ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A processor, comprising: one or more circuits totrain one or more neural networks to select a label from one or morelabels available for a training image, wherein the selected label andthe training image is to train the one or more neural networks.
 2. Theprocessor of claim 1, wherein training the one or more neural networksfurther comprises the one or more circuits to: obtain a batch of datacomprising a portion of the one or more labels and a plurality oftraining images; generate a set of weights for the one or more neuralnetworks by processing the batch; compute, using the set of weights forthe one or more neural networks, a set of features representingcharacteristics corresponding to a label set of the batch of data;concatenate the features to generate a feature set; compute a weight foreach feature set; compute a weighted average, using the weight for eachfeature set, for the portion of the one or more labels; and select thelabel, based on the weighted average for the portion of the one or morelabels, to update the one or more neural networks.
 3. The processor ofclaim 2, wherein the weights for each feature set is computed throughone or more fully-connected layers and one or more activation functions.4. The processor of claim 1, wherein the one or more labels aregenerated by different algorithm-based labelers.
 5. The processor ofclaim 1, where the one or more circuits are further to: perform acomparison of an output of the one or more neural networks being trainedwith labels with the one or more labels; and select the label based atleast in part on the comparison.
 6. The processor of claim 5, whereinthe output is compared through one or more binary cross entropy (BCE)loss functions.
 7. The processor of claim 1, wherein the one or moreneural networks uses the selected label and the training image toperform one or more image classification tasks.
 8. A system, comprising:one or more computers having one or more processors to train one or moreneural networks to select labels to be used while training the one ormore neural networks.
 9. The system of claim 8, wherein training the oneor more neural networks further comprises the one or more processors to:provide a plurality of images associated with the labels to the one ormore neural networks; and select the labels for an image of theplurality of images to train the one or more neural networks.
 10. Thesystem of claim 9, wherein selecting the labels for the image of theplurality of images to train the one or more neural networks furthercomprises the one or more processors to: receive a portion of trainingdata comprising one or more labels available for the image; generate aset of weights for the one or more neural networks by processing theportion; compute, using the set of weights, features representingcharacteristics corresponding to the labels; use the features to computeweighted averages for the labels; and select the labels from the labelsbased on the weighted averages.
 11. The system of claim 10, wherein theset of weights for the one or more neural networks are generated throughone or more back-propagation processes.
 12. The system of claim 10,wherein one or more differentiable binarization processes are performedon the weighted averages.
 13. The system of claim 8, wherein the labelsare generated by different natural language processing (NLP) algorithmsexecuted by different computing resources.
 14. A machine-readable mediumhaving stored thereon a set of instructions, which if performed by oneor more processors, cause the one or more processors to train one ormore neural networks to select labels to be used while training the oneor more neural networks.
 15. The machine-readable medium of claim 14,wherein training the one or more neural networks further causes the oneor more processors to: receive data as input to train the one or moreneural networks; generate a set of model weights for the one or moreneural networks by processing a portion of the data, wherein the portionof the data comprises one or more labels available for a training image;compute, based on the set of model weights, a set of featuresrepresenting characteristics corresponding to a label set of theportion; use the set of features to compute weights for each label fromthe label set; and select the labels from the label set based on theweights to update parameters of the one or more neural networks.
 16. Themachine-readable medium of claim 15, wherein training the one or moreneural networks further causes the one or more processors to: perform acomparison of with an output of the one or more neural networks with thelabel set; and select the labels based in part on the comparison. 17.The machine-readable medium of claim 16, wherein the comparison isperformed using one or more loss functions.
 18. The machine-readablemedium of claim 15, wherein the one or more labels are generated by oneor more algorithm-based labelers.
 19. The machine-readable medium ofclaim 15, wherein the selected labels and the training image train theone or more neural networks to perform one or more multi-labelclassification tasks.
 20. The machine-readable medium of claim 15,wherein the set of model weights are generated through one or moreback-propagation processes.
 21. A processor, comprising: one or morecircuits to use a neural network to infer information, wherein theneural network is trained by selecting labels to be used while trainingthe neural network.
 22. The processor of claim 21, wherein the one ormore circuits are further to: determine a batch from training data,received as input to the neural network, comprising one or more labelsavailable for a training image; generate model weights for the neuralnetwork by processing the batch; compute, using the model weights,features representing characteristics of the one or more labels; use thefeatures to compute weighted averages for the one or more labels; andselect the labels from the one or more labels based on the weightedaverages.
 23. The processor of claim 22, wherein the one or morecircuits are further to update parameters of the neural network usingthe selected labels to perform image segmentation tasks.
 24. Theprocessor of claim 22, wherein training data comprises a plurality oftraining images, wherein each of the plurality of training images isaccompanied with a text report.
 25. The processor of claim 24, whereinthe one or more circuits are further to: generate text embedding basedon the text report; and use the text embedding to identify the one ormore labels.
 26. The processor of claim 21, wherein the neural networkis trained by selecting labels to be used while simultaneously trainingthe neural network to classify training images and associated labels.27. A system, comprising: one or more computers having one or moreprocessors to use a neural network to infer information, wherein theneural network is trained by selecting labels to be used while trainingthe neural network.
 28. The system of claim 27, further comprising oneor more computers having one or more processors to train the neuralnetwork to select labels while simultaneously training the neuralnetwork to classify training images.
 29. The system of claim 28, whereintraining the neural network to select labels further comprises the oneor more processors to: receive data as input to the neural network;generate a set of weights for the neural network by processing a subsetof the data, wherein the subset of the data comprises one or more labelsavailable for a training image; compute, based on the set of weights, aset of features representing characteristics corresponding to a labelset of the subset; use the set of features to compute weights for eachlabel from the subset; and update a component of the neural networkusing a portion of the labels from the label set based on the computedweights for each label.
 30. The system of claim 29, wherein the one ormore labels are generated by one or more natural-language processing(NLP) algorithms performed by different processors.
 31. The system ofclaim 30, wherein the portion of the labels and the training image areused by the neural network to perform one or more image classificationtasks.
 32. A machine-readable medium having stored thereon a set ofinstructions, which if performed by one or more processors, cause theone or more processors to use a neural network to infer information,wherein the neural network is trained by selecting labels to be usedwhile training the neural network.
 33. The machine-readable medium ofclaim 32, wherein training the neural network further causes the one ormore processors to: use training data, received as input to the neuralnetwork, comprising a plurality of labels available for a plurality oftraining images to determine labels for a training image; and use thelabels for the training image to update parameters of the neuralnetwork.
 34. The machine-readable medium of claim 33, wherein parametersof the neural network is updated based on results from performing acomparison of the labels with an output of the neural network.
 35. Themachine-readable medium of claim 33, wherein the training datacomprising a plurality of labels for a plurality of images is extractedfrom text reports accompanying each training image of the plurality ofimages.
 36. The machine-readable medium of claim 35, wherein textreports accompanying each training image of the plurality of images aregenerated by one or more natural-language processing (NLP) algorithmsperformed by different computing resources.